rm(list=ls())
gc()
setwd("/hpc/group/pbenfeylab/CheWei/CW_data/genesys/")

A matrix: 2 x 6 of type dbl

	used	(Mb)	gc trigger	(Mb)	max used	(Mb)
Ncells	625266	33.4	1361470	72.8	1102000	58.9
Vcells	1159075	8.9	8388608	64.0	1802279	13.8

## Need seu4
suppressMessages(library(Seurat))
suppressMessages(library(cowplot))
suppressMessages(library(scattermore))
suppressMessages(library(scater))
suppressMessages(library(cowplot))
suppressMessages(library(RColorBrewer))
suppressMessages(library(grid))
suppressMessages(library(gplots))
suppressMessages(library(circular))
suppressMessages(library(ggplot2))
suppressMessages(library(ggnewscale))
suppressMessages(library(tidyverse))
suppressMessages(library(ComplexHeatmap))
suppressMessages(library(circlize))
suppressMessages(library(patchwork))

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: AlmaLinux 9.3 (Shamrock Pampas Cat)

Matrix products: default
BLAS/LAPACK: /hpc/group/pbenfeylab/ch416/miniconda3/envs/seu4/lib/libopenblasp-r0.3.21.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] patchwork_1.1.3             circlize_0.4.15            
 [3] ComplexHeatmap_2.14.0       forcats_0.5.2              
 [5] stringr_1.5.1               dplyr_1.1.3                
 [7] purrr_1.0.2                 readr_2.1.3                
 [9] tidyr_1.3.0                 tibble_3.2.1               
[11] tidyverse_1.3.2             ggnewscale_0.4.8           
[13] circular_0.4-95             gplots_3.1.3               
[15] RColorBrewer_1.1-3          scater_1.26.1              
[17] ggplot2_3.4.4               scuttle_1.8.0              
[19] SingleCellExperiment_1.20.0 SummarizedExperiment_1.28.0
[21] Biobase_2.58.0              GenomicRanges_1.50.0       
[23] GenomeInfoDb_1.34.8         IRanges_2.32.0             
[25] S4Vectors_0.36.0            BiocGenerics_0.44.0        
[27] MatrixGenerics_1.10.0       matrixStats_1.1.0          
[29] scattermore_1.2             cowplot_1.1.1              
[31] SeuratObject_4.1.3          Seurat_4.1.1.9001          

loaded via a namespace (and not attached):
  [1] utf8_1.2.4                spatstat.explore_3.2-5   
  [3] reticulate_1.34.0         tidyselect_1.2.0         
  [5] htmlwidgets_1.6.2         BiocParallel_1.32.5      
  [7] Rtsne_0.16                munsell_0.5.0            
  [9] ScaledMatrix_1.6.0        codetools_0.2-19         
 [11] ica_1.0-3                 pbdZMQ_0.3-8             
 [13] future_1.33.0             miniUI_0.1.1.1           
 [15] withr_2.5.2               spatstat.random_3.2-1    
 [17] colorspace_2.1-0          progressr_0.14.0         
 [19] uuid_1.1-0                ROCR_1.0-11              
 [21] tensor_1.5                listenv_0.9.0            
 [23] repr_1.1.4                GenomeInfoDbData_1.2.9   
 [25] polyclip_1.10-6           parallelly_1.36.0        
 [27] vctrs_0.6.4               generics_0.1.3           
 [29] timechange_0.1.1          doParallel_1.0.17        
 [31] R6_2.5.1                  clue_0.3-64              
 [33] ggbeeswarm_0.7.1          rsvd_1.0.5               
 [35] bitops_1.0-7              spatstat.utils_3.0-4     
 [37] DelayedArray_0.24.0       assertthat_0.2.1         
 [39] promises_1.2.1            scales_1.2.1             
 [41] googlesheets4_1.0.1       beeswarm_0.4.0           
 [43] gtable_0.3.4              beachmat_2.14.0          
 [45] globals_0.16.2            goftest_1.2-3            
 [47] rlang_1.1.2               GlobalOptions_0.1.2      
 [49] splines_4.2.2             lazyeval_0.2.2           
 [51] gargle_1.2.1              spatstat.geom_3.2-7      
 [53] broom_1.0.2               modelr_0.1.10            
 [55] reshape2_1.4.4            abind_1.4-5              
 [57] backports_1.4.1           httpuv_1.6.12            
 [59] tools_4.2.2               ellipsis_0.3.2           
 [61] ggridges_0.5.4            Rcpp_1.0.11              
 [63] plyr_1.8.9                base64enc_0.1-3          
 [65] sparseMatrixStats_1.10.0  zlibbioc_1.44.0          
 [67] RCurl_1.98-1.6            deldir_1.0-9             
 [69] GetoptLong_1.0.5          pbapply_1.7-2            
 [71] viridis_0.6.4             zoo_1.8-12               
 [73] haven_2.5.1               ggrepel_0.9.4            
 [75] cluster_2.1.4             fs_1.6.3                 
 [77] magrittr_2.0.3            data.table_1.14.8        
 [79] RSpectra_0.16-1           reprex_2.0.2             
 [81] lmtest_0.9-40             RANN_2.6.1               
 [83] googledrive_2.0.0         mvtnorm_1.1-3            
 [85] fitdistrplus_1.1-11       hms_1.1.2                
 [87] mime_0.12                 evaluate_0.23            
 [89] xtable_1.8-4              readxl_1.4.1             
 [91] shape_1.4.6               fastDummies_1.7.3        
 [93] gridExtra_2.3             compiler_4.2.2           
 [95] KernSmooth_2.23-20        crayon_1.5.2             
 [97] htmltools_0.5.7           tzdb_0.3.0               
 [99] later_1.3.1               lubridate_1.9.0          
[101] DBI_1.1.3                 dbplyr_2.2.1             
[103] MASS_7.3-58.3             boot_1.3-28.1            
[105] Matrix_1.5-4              cli_3.6.1                
[107] parallel_4.2.2            igraph_1.5.1             
[109] pkgconfig_2.0.3           sp_2.1-1                 
[111] IRdisplay_1.1             plotly_4.10.3            
[113] spatstat.sparse_3.0-3     foreach_1.5.2            
[115] xml2_1.3.3                vipor_0.4.5              
[117] XVector_0.38.0            rvest_1.0.3              
[119] digest_0.6.33             sctransform_0.4.1        
[121] RcppAnnoy_0.0.21          spatstat.data_3.0-3      
[123] cellranger_1.1.0          leiden_0.4.3             
[125] uwot_0.1.16               DelayedMatrixStats_1.20.0
[127] shiny_1.7.5.1             gtools_3.9.4             
[129] rjson_0.2.21              lifecycle_1.0.4          
[131] nlme_3.1-162              jsonlite_1.8.7           
[133] BiocNeighbors_1.16.0      viridisLite_0.4.2        
[135] fansi_1.0.5               pillar_1.9.0             
[137] lattice_0.21-8            fastmap_1.1.1            
[139] httr_1.4.7                survival_3.4-0           
[141] glue_1.6.2                iterators_1.0.14         
[143] png_0.1-8                 stringi_1.8.1            
[145] RcppHNSW_0.5.0            BiocSingular_1.14.0      
[147] caTools_1.18.2            IRkernel_1.3.1.9000      
[149] irlba_2.3.5.1             future.apply_1.11.0      

Load TF list

wanted_TFs <- read.csv("./Kay_TF_thalemine_annotations.csv")

nrow(wanted_TFs)

2484

## Make TF names unique
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G33880"]="WOX9"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G45160"]="SCL27"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G04410"]="NAC78"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G29035"]="ORS1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G02540"]="ZHD3"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G16500"]="IAA26"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G09740"]="HAG5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT4G24660"]="ZHD2"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G46880"]="HDG5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G28420"]="RLT1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G14580"]="BLJ"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G45260"]="BIB"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G02070"]="RVN"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G28160"]="FIT"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G68360"]="GIS3"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G20640"]="NLP4"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G05550"]="VFP5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G59470"]="FRF1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G15150"]="HAT7"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G14750"]="WER"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G75710"]="BRON"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G74500"]="TMO7"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G12646"]="RITF1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G48100"]="ARR5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT4G16141"]="GATA17L"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G65640"]="NFL"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G62700"]="VND5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT4G36160"]="VND2"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G66300"]="VND3"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G12260"]="VND4"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G62380"]="VND6"

## TTG1
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G24520"]

'TTG1'

## SCRAMBLED
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G11130"]

## CAPRICE
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G46410"]

'CPC'

Load GRN centrality scores for each transition

stem2pro <- read.csv("./TF_GRN_centrality_t0-t1_zscore3.csv")
pro2trans <- read.csv("./TF_GRN_centrality_t1-t3_zscore3.csv")
trans2el <- read.csv("./TF_GRN_centrality_t3-t5_zscore3.csv")
el2el <- read.csv("./TF_GRN_centrality_t5-t7_zscore3.csv")
el2mat <- read.csv("./TF_GRN_centrality_t7-t9_zscore3.csv")

head(stem2pro)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	GAMMA-H2AX	10	1.0481586	0.35127479	0.69688385	0.8173690	0.002480878	0.12299414	0.74551971	0.12903226	...	0.09969168	0.6445045	0.0003312703	0.05985332	0.2263711	0.18553092	0.04084014	0.0001635787	0.0005359491	0.06437981
2	HB-2	9	0.2521246	0.15580737	0.09631728	0.3296259	0.001392521	0.05177987	0.05017921	0.01792115	...	0.20760534	0.9541161	0.0004221278	0.08107215	0.4037340	0.15985998	0.24387398	0.8044362534	0.0006764851	0.08090327
3	CRF2	9	0.7535411	0.45892351	0.29461756	0.7579030	0.002018580	0.10303448	1.05376344	0.61648746	...	0.14799589	0.9041508	0.0003743185	0.06718402	1.2240373	0.84014002	0.38389732	0.9883559254	0.0007417819	0.12383249
4	HDA3	8	0.8583569	0.05665722	0.80169972	0.9935778	0.002648215	0.11849391	1.27956989	0.44802867	...	0.00000000	0.0000000	0.0000000000	0.00000000	0.3080513	0.23103851	0.07701284	0.1441496090	0.0006045867	0.06877130
5	HMGB6	8	0.3201133	0.27762040	0.04249292	0.0000000	0.002007357	0.07062791	0.39068100	0.26164875	...	0.06166495	0.8344763	0.0003372956	0.04870876	0.1155193	0.09801634	0.01750292	0.0000000000	0.0006302146	0.04251342
6	HAT1	8	0.3767705	0.15297450	0.22379603	0.2972653	0.001926347	0.07873702	0.13261649	0.11827957	...	0.28365879	0.9619246	0.0004216832	0.08605297	0.4714119	0.35355893	0.11785298	0.7370009488	0.0007078086	0.08331075

head(pro2trans)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	HAT1	8	0.4607843	0.27647059	0.18431373	3.133403e-02	0.001327612	0.08696905	0.04144385	0.00802139	...	0.038500507	0.203569322	0.0003311874	0.022256053	0.15838150	0.06820809	0.09017341	0.000000000	0.0002910342	0.04766973
2	GAMMA-H2AX	8	0.7196078	0.29215686	0.42745098	9.884241e-01	0.001408803	0.09728463	0.21657754	0.09358289	...	0.016210740	0.000000000	0.0002320833	0.020539257	0.00000000	0.00000000	0.00000000	0.000000000	0.0000000000	0.00000000
3	GRP2B	8	0.2745098	0.14901961	0.12549020	3.852229e-05	0.001301947	0.06493313	0.07486631	0.06283422	...	0.332320162	0.003706398	0.0003966881	0.080845333	0.27514451	0.18381503	0.09132948	0.044570221	0.0003646495	0.06817366
4	BME3	7	0.1372549	0.07647059	0.06078431	1.896105e-01	0.001088849	0.03910060	0.11363636	0.07352941	...	0.032421479	0.037009521	0.0002207693	0.018088461	0.04855491	0.02658960	0.02196532	0.003753211	0.0002726701	0.02049709
5	GATA2	7	1.2980392	0.56862745	0.72941176	9.796833e-01	0.001664178	0.12572860	1.54946524	0.95989305	...	0.001013171	0.003943764	0.0001739663	0.003335369	0.09942197	0.04508671	0.05433526	0.078709056	0.0003095810	0.03070615
6	HB20	7	0.4019608	0.01764706	0.38431373	1.637929e-01	0.001266621	0.08316733	0.05614973	0.00802139	...	0.098277609	0.070318810	0.0003035202	0.044232294	0.20115607	0.18843931	0.01271676	0.001540088	0.0003809927	0.05735016

head(trans2el)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	HB20	8	0.29893238	0.12099644	0.177935943	0.7943333270	0.0005577957	0.10535727	0.231040564	0.194003527	...	0.06226650	0.16547672	0.0003305909	0.041245684	0.00000000	0.00000000	0.00000000	0.000000e+00	0.0000000000	0.00000000
2	AT4G30410	8	0.70106762	0.10142349	0.599644128	0.5661090706	0.0005652264	0.16819541	1.223985891	0.895943563	...	0.02739726	0.02991742	0.0003475172	0.036212288	0.00000000	0.00000000	0.00000000	0.000000e+00	0.0000000000	0.00000000
3	UPB1	7	0.85587189	0.03736655	0.818505338	0.3010669813	0.0006138503	0.18742391	0.089947090	0.037037037	...	0.04732254	0.00000000	0.0003551783	0.044442949	0.05555556	0.04678363	0.00877193	0.000000e+00	0.0005728614	0.02830553
4	BZIP61	7	0.14234875	0.07829181	0.064056940	0.5917622953	0.0004299594	0.06355916	0.068783069	0.061728395	...	0.09589041	0.67429030	0.0004204217	0.062160969	0.00000000	0.00000000	0.00000000	0.000000e+00	0.0000000000	0.00000000
5	HAT1	6	0.01779359	0.01601423	0.001779359	0.0000000000	0.0003900365	0.01711689	0.008818342	0.007054674	...	0.01867995	0.47303907	0.0004062287	0.051264227	0.07163743	0.06286550	0.00877193	0.000000e+00	0.0005106317	0.03622616
6	AT1G74840	6	0.16725979	0.10142349	0.065836299	0.0008532044	0.0005357602	0.07918305	0.052910053	0.042328042	...	0.00124533	0.00000000	0.0002016686	0.003987783	0.10672515	0.08479532	0.02192982	3.638917e-05	0.0008461764	0.04511538

head(el2el)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	UPB1	9	0.4441558	0.21818182	0.22597403	0.426102543	0.0005882946	0.11339911	0.06851852	0.03703704	...	0.043907794	1.172483e-02	0.0002748839	0.04073606	0.17448680	0.16129032	0.01319648	0.001937809	0.0007443138	0.06616948
2	HAT22	9	0.2831169	0.06493506	0.21818182	0.358887987	0.0005878071	0.09088137	0.40370370	0.11851852	...	0.016465423	5.186910e-05	0.0002271318	0.02549151	0.08651026	0.07038123	0.01612903	0.052760086	0.0007253234	0.03748769
3	WLIM1	8	0.1246753	0.01038961	0.11428571	0.376048431	0.0004643862	0.05776708	0.11666667	0.03518519	...	0.062568606	1.502756e-02	0.0003027703	0.05051630	0.00000000	0.00000000	0.00000000	0.000000000	0.0000000000	0.00000000
4	AT4G30410	7	0.2000000	0.13246753	0.06753247	0.005756223	0.0005111366	0.07034153	0.78518519	0.65555556	...	0.007683864	2.050639e-05	0.0002316630	0.02502566	0.00000000	0.00000000	0.00000000	0.000000000	0.0000000000	0.00000000
5	LEP	7	0.7636364	0.69610390	0.06753247	0.296340639	0.0006628421	0.15817165	0.82962963	0.75185185	...	0.019758507	1.952811e-02	0.0002128259	0.02351468	0.00000000	0.00000000	0.00000000	0.000000000	0.0000000000	0.00000000
6	GBF6	6	0.0000000	0.00000000	0.00000000	0.000000000	0.0000000000	0.00000000	0.00000000	0.00000000	...	0.506037322	3.889941e-02	0.0003675904	0.13792428	0.00000000	0.00000000	0.00000000	0.000000000	0.0000000000	0.00000000

head(el2mat)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	AXR3	8	0.7809798	0.1354467	0.64553314	0.9945362	0.0008515654	0.12600157	0.00000000	0.00000000	...	0.004592423	1.807936e-04	0.0001297446	0.006055279	0.78768577	0.59023355	0.19745223	0.019943985	0.0014164778	0.11644417
2	HB-7	6	0.1873199	0.1671470	0.02017291	0.0000000	0.0007831216	0.06742666	0.10954064	0.08833922	...	0.011481056	2.630086e-03	0.0002391847	0.025527182	0.06794055	0.02547771	0.04246285	0.000000000	0.0006447161	0.02364832
3	RAV1	6	0.4726225	0.1930836	0.27953890	0.6033216	0.0007660186	0.10581265	0.20494700	0.03886926	...	0.026406429	1.187695e-05	0.0002787072	0.041845397	0.12101911	0.08492569	0.03609342	0.004223698	0.0007212557	0.04033330
4	HAT22	6	0.9798271	0.7146974	0.26512968	0.5695391	0.0010049655	0.14327323	0.43462898	0.16254417	...	0.009184845	0.000000e+00	0.0002073287	0.014163359	0.05732484	0.03397028	0.02335456	0.014008222	0.0005989863	0.02039925
5	RD26	6	1.0288184	0.5734870	0.45533141	0.7858523	0.0008992893	0.14485878	0.48409894	0.24028269	...	0.040183697	2.441374e-04	0.0002800148	0.044217242	0.00000000	0.00000000	0.00000000	0.000000000	0.0000000000	0.00000000
6	MYB7	6	0.6195965	0.3688761	0.25072046	0.4572721	0.0008324031	0.12487370	0.08480565	0.02120141	...	0.000000000	0.000000e+00	0.0000000000	0.000000000	0.00000000	0.00000000	0.00000000	0.000000000	0.0000000000	0.00000000

head(trans2el)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	HB20	8	0.29893238	0.12099644	0.177935943	0.7943333270	0.0005577957	0.10535727	0.231040564	0.194003527	...	0.06226650	0.16547672	0.0003305909	0.041245684	0.00000000	0.00000000	0.00000000	0.000000e+00	0.0000000000	0.00000000
2	AT4G30410	8	0.70106762	0.10142349	0.599644128	0.5661090706	0.0005652264	0.16819541	1.223985891	0.895943563	...	0.02739726	0.02991742	0.0003475172	0.036212288	0.00000000	0.00000000	0.00000000	0.000000e+00	0.0000000000	0.00000000
3	UPB1	7	0.85587189	0.03736655	0.818505338	0.3010669813	0.0006138503	0.18742391	0.089947090	0.037037037	...	0.04732254	0.00000000	0.0003551783	0.044442949	0.05555556	0.04678363	0.00877193	0.000000e+00	0.0005728614	0.02830553
4	BZIP61	7	0.14234875	0.07829181	0.064056940	0.5917622953	0.0004299594	0.06355916	0.068783069	0.061728395	...	0.09589041	0.67429030	0.0004204217	0.062160969	0.00000000	0.00000000	0.00000000	0.000000e+00	0.0000000000	0.00000000
5	HAT1	6	0.01779359	0.01601423	0.001779359	0.0000000000	0.0003900365	0.01711689	0.008818342	0.007054674	...	0.01867995	0.47303907	0.0004062287	0.051264227	0.07163743	0.06286550	0.00877193	0.000000e+00	0.0005106317	0.03622616
6	AT1G74840	6	0.16725979	0.10142349	0.065836299	0.0008532044	0.0005357602	0.07918305	0.052910053	0.042328042	...	0.00124533	0.00000000	0.0002016686	0.003987783	0.10672515	0.08479532	0.02192982	3.638917e-05	0.0008461764	0.04511538

min_max_normalize <- function(data) {
  min_val <- min(data)
  max_val <- max(data)
  normalized_data <- (data - min_val) / (max_val - min_val)
  return(normalized_data)
}

summary(stem2pro$tri_betweenness_centrality)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.02406 0.00000 0.98597 

summary(min_max_normalize(stem2pro$tri_betweenness_centrality))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.0244  0.0000  1.0000 

ncol(stem2pro)

62

stem2pro <- as.data.frame(cbind(stem2pro[,1],apply(stem2pro[,3:62],2,min_max_normalize)))
pro2trans <- as.data.frame(cbind(pro2trans[,1],apply(pro2trans[,3:62],2,min_max_normalize)))
trans2el <- as.data.frame(cbind(trans2el[,1],apply(trans2el[,3:62],2,min_max_normalize)))
el2el <- as.data.frame(cbind(el2el[,1],apply(el2el[,3:62],2,min_max_normalize)))
el2mat <- as.data.frame(cbind(el2mat[,1],apply(el2mat[,3:62],2,min_max_normalize)))

dat <- stem2pro %>%
full_join(pro2trans, by = "V1") %>%
full_join(trans2el, by = "V1") %>%
full_join(el2el, by = "V1") %>%
full_join(el2mat, by = "V1")

dat[is.na(dat)] <- 0

n <- c('atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality',
       'tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality',
       'lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality',
       'cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality',
       'end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality',
       'per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality',
       'pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality',
       'xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality',
       'phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality',
       'col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality')

colnames(dat) <- c("TF",gsub("$","_1",n), gsub("$","_2",n),gsub("$","_3",n),gsub("$","_4",n),gsub("$","_5",n))

GeneID <- wanted_TFs$GeneID[match(dat$TF, wanted_TFs$Name)]

dat <- cbind(GeneID, dat)

head(dat)

A data.frame: 6 x 302

	GeneID	TF	atri_degree_centrality_1	atri_out_centrality_1	atri_in_centrality_1	atri_betweenness_centrality_1	atri_closeness_centrality_1	atri_eigenvector_centrality_1	tri_degree_centrality_1	tri_out_centrality_1	...	phl_in_centrality_5	phl_betweenness_centrality_5	phl_closeness_centrality_5	phl_eigenvector_centrality_5	col_degree_centrality_5	col_out_centrality_5	col_in_centrality_5	col_betweenness_centrality_5	col_closeness_centrality_5	col_eigenvector_centrality_5
	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	...	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>
1	AT1G54690	GAMMA-H2AX	0.719844357976654	0.382716049382716	0.869257950530035	0.822652238008068	0.927975058549276	0.922339387205014	0.533333333333333	0.143426294820717	...	0.00458715596330275	0	0.461497006723908	0.0361027357633705	0	0	0	0	0	0
2	AT4G16780	HB-2	0.173151750972763	0.169753086419753	0.120141342756184	0.331756548785822	0.520874058755548	0.388299883582212	0.0358974358974359	0.0199203187250996	...	0.00114678899082569	0	0.459827872532017	0.0361027357633705	0	0	0	0	0	0
3	AT4G23750	CRF2	0.517509727626459	0.5	0.367491166077739	0.762801924541139	0.755052116281518	0.772660846729075	0.753846153846154	0.685258964143426	...	0.0160550458715596	0	0.646169693735806	0.152197951166501	0	0	0	0	0	0
4	AT3G44750	HDA3	0.589494163424125	0.0617283950617284	1	1	0.990567579310647	0.88859191221792	0.915384615384615	0.49800796812749	...	0	0	0	0	0	0	0	0	0	0
5	AT5G23420	HMGB6	0.219844357976654	0.302469135802469	0.0530035335689046	0	0.75085417652198	0.529642376608434	0.279487179487179	0.290836653386454	...	0	0	0	0	0	0	0	0	0	0
6	AT4G17460	HAT1	0.25875486381323	0.166666666666667	0.279151943462898	0.299186767969674	0.720552196291768	0.590452970620055	0.0948717948717949	0.131474103585657	...	0.00344036697247706	0	0.481357647786302	0.0361027357633705	0	0	0	0	0	0

numz <- function(x){
    sum(x==0)/length(x)
}

dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric)))
dat$celltype_specificity <- min_max_normalize(apply(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric),1,numz))
dat$weighted_score <- dat$combined_score + dat$celltype_specificity
dat <- dat %>% arrange(desc(weighted_score))

head(dat)

A data.frame: 6 x 305

	GeneID	TF	atri_degree_centrality_1	atri_out_centrality_1	atri_in_centrality_1	atri_betweenness_centrality_1	atri_closeness_centrality_1	atri_eigenvector_centrality_1	tri_degree_centrality_1	tri_out_centrality_1	...	phl_eigenvector_centrality_5	col_degree_centrality_5	col_out_centrality_5	col_in_centrality_5	col_betweenness_centrality_5	col_closeness_centrality_5	col_eigenvector_centrality_5	combined_score	celltype_specificity	weighted_score
	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	...	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>
1	AT5G24800	BZIP9	0	0	0	0	0	0	0	0	...	0.991510602104265	0	0	0	0	0	0	0.7446064	0.6120690	1.356675
2	AT3G43430	AT3G43430	0	0	0	0	0	0	0	0	...	0.635753571515156	0	0	0	0	0	0	0.7633960	0.4698276	1.233224
3	AT3G20840	PLT1	0.447470817120623	0.216049382716049	0.565371024734982	0.963922953555055	0.802246769591505	0.771509226624259	0	0	...	0	0.0614035087719298	0.0997566909975669	0.0022075055187638	0	0.590409461781574	0.239091323851849	0.5610944	0.6681034	1.229198
4	AT5G15150	HAT7	0	0	0	0	0	0	0.228205128205128	0.051792828685259	...	0	0	0	0	0	0	0	0.8650723	0.3534483	1.218521
5	AT5G57620	MYB36	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0.5176350	0.6810345	1.198669
6	AT2G45050	GATA2	0.817120622568093	0.79320987654321	0.575971731448763	0.804800012959873	0.834697097968316	0.913662678649798	0.779487179487179	0.665338645418327	...	0	0	0	0	0	0	0	0.8562137	0.3146552	1.170869

write.csv(dat,"TF_GRN_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=FALSE)

dat_genesys <- dat

Load GRN centrality scores for each transition (RAW)

stem2pro <- read.csv("./Raw_TF_GRN_centrality_t0-t1_zscore3.csv")
pro2trans <- read.csv("./Raw_TF_GRN_centrality_t1-t3_zscore3.csv")
trans2el <- read.csv("./Raw_TF_GRN_centrality_t3-t5_zscore3.csv")
el2el <- read.csv("./Raw_TF_GRN_centrality_t5-t7_zscore3.csv")
el2mat <- read.csv("./Raw_TF_GRN_centrality_t7-t9_zscore3.csv")

head(stem2pro)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	PLT1	0	1.0725806	0.9973118	0.07526882	0.30811813	0.0004084590	0.1539063	0.0000000	0.00000000	...	0.0000000	0.00000000	0.0000000000	0.0000000	1.9272959	0.9783163	0.94897959	0.1421208330	0.0002642681	0.11516732
2	AT4G00238	0	0.6182796	0.2392473	0.37903226	0.11115700	0.0002986954	0.1204221	0.1761006	0.14465409	...	0.0000000	0.00000000	0.0000000000	0.0000000	0.3903061	0.1938776	0.19642857	0.0002524956	0.0002045034	0.06845200
3	WER	0	1.1720430	0.2204301	0.95161290	0.09764368	0.0004069432	0.1523170	0.1698113	0.09119497	...	0.0000000	0.00000000	0.0000000000	0.0000000	0.3163265	0.2946429	0.02168367	0.0001352073	0.0002145360	0.06484553
4	BRON	0	0.9489247	0.5483871	0.40053763	0.09401356	0.0003483373	0.1390706	0.3647799	0.35849057	...	0.0000000	0.00000000	0.0000000000	0.0000000	1.3928571	0.7831633	0.60969388	0.0360759246	0.0002442187	0.11264577
5	HDA3	0	1.1397849	0.2956989	0.84408602	0.08606498	0.0003858371	0.1485553	0.5000000	0.20440252	...	0.0000000	0.00000000	0.0000000000	0.0000000	0.5089286	0.4744898	0.03443878	0.0004170250	0.0002276166	0.08153638
6	CRF2	0	0.8682796	0.2177419	0.65053763	0.07658030	0.0002937998	0.1400685	0.1918239	0.13207547	...	0.4408428	0.02174062	0.0001168012	0.1547572	0.6020408	0.2448980	0.35714286	0.0047420309	0.0002081976	0.08432017

head(pro2trans)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	GAMMA-H2AX	0	0.71370968	0.37096774	0.34274194	0.8750367	4.554611e-05	0.15307401	0.645762712	0.569491525	...	0.013347023	0.01188559	0.0004332084	0.06913446	0.004535147	0.002267574	0.002267574	0.000000e+00	0.0001006084	0.002564691
2	GATA2	0	1.22580645	0.45967742	0.76612903	0.8334352	5.202446e-05	0.19592261	0.815254237	0.571186441	...	0.000000000	0.00000000	0.0000000000	0.00000000	0.078231293	0.071428571	0.006802721	5.147727e-06	0.0001464426	0.026671998
3	MYB97	0	0.05040323	0.03629032	0.01411290	0.8271098	2.501421e-05	0.02270513	0.008474576	0.003389831	...	0.002053388	0.00000000	0.0002402703	0.00948676	0.000000000	0.000000000	0.000000000	0.000000e+00	0.0000000000	0.000000000
4	ATS	0	0.76411290	0.20766129	0.55645161	0.8270283	4.461151e-05	0.17160528	0.525423729	0.213559322	...	0.000000000	0.00000000	0.0000000000	0.00000000	0.010204082	0.007936508	0.002267574	0.000000e+00	0.0001039290	0.004417425
5	AT5G25470	0	0.20161290	0.11290323	0.08870968	0.8182063	4.029434e-05	0.07557589	0.000000000	0.000000000	...	0.000000000	0.00000000	0.0000000000	0.00000000	0.154195011	0.058956916	0.095238095	3.717894e-01	0.0001510470	0.038240868
6	AT4G00990	0	0.07862903	0.01814516	0.06048387	0.8158439	3.210625e-05	0.03533563	0.000000000	0.000000000	...	0.015400411	0.00000000	0.0002675993	0.01503971	0.229024943	0.166666667	0.062358277	8.241897e-01	0.0001686898	0.054988753

head(trans2el)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	GL2	0	0.62137405	0.28396947	0.33740458	0.5345332	0.0002196232	0.15420347	0.000000000	0.000000000	...	0.000000000	0.00000000	0.0000000000	0.000000000	0.00000000	0.00000000	0.000000000	0.000000e+00	0.0000000000	0.000000000
2	DAR7	0	0.16335878	0.11603053	0.04732824	0.4940939	0.0001959232	0.08874067	0.000000000	0.000000000	...	0.000000000	0.00000000	0.0000000000	0.000000000	0.02222222	0.01307190	0.009150327	5.817336e-05	0.0002597563	0.007458697
3	MYB47	0	0.02900763	0.01068702	0.01832061	0.4938371	0.0001377658	0.01525216	0.000000000	0.000000000	...	0.000000000	0.00000000	0.0000000000	0.000000000	0.00000000	0.00000000	0.000000000	0.000000e+00	0.0000000000	0.000000000
4	AT4G31650	0	0.28549618	0.14045802	0.14503817	0.4744216	0.0001875331	0.11017412	0.007751938	0.005813953	...	0.000000000	0.00000000	0.0000000000	0.000000000	0.00000000	0.00000000	0.000000000	0.000000e+00	0.0000000000	0.000000000
5	ATCTH	0	0.24427481	0.20916031	0.03511450	0.4562504	0.0002128285	0.10630778	0.197674419	0.174418605	...	0.012707722	0.03379512	0.0001984223	0.009559577	0.31633987	0.16732026	0.149019608	5.197276e-02	0.0003783489	0.068248499
6	IAA1	0	0.11450382	0.07175573	0.04274809	0.4180451	0.0001688357	0.05810938	0.013565891	0.011627907	...	0.007820137	0.00000000	0.0002107661	0.008177595	0.04183007	0.02352941	0.018300654	0.000000e+00	0.0003085903	0.015204464

head(el2el)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	WRKY13	0	0.11069418	0.03752345	0.07317073	0.9134421	0.0002226774	0.04189534	0.000000000	0.000000000	...	0	0	0	0	0.00000000	0.00000000	0.000000000	0.00000e+00	0.000000000	0.00000000
2	AT1G64380	0	0.23264540	0.05253283	0.18011257	0.8604685	0.0002563892	0.07563698	0.009324009	0.006993007	...	0	0	0	0	0.00000000	0.00000000	0.000000000	0.00000e+00	0.000000000	0.00000000
3	NAC6	0	0.75046904	0.26454034	0.48592871	0.8480477	0.0003210821	0.15026692	0.034965035	0.013986014	...	0	0	0	0	0.00000000	0.00000000	0.000000000	0.00000e+00	0.000000000	0.00000000
4	MC2	0	0.22326454	0.03939962	0.18386492	0.8402996	0.0002426789	0.07422758	0.000000000	0.000000000	...	0	0	0	0	0.00000000	0.00000000	0.000000000	0.00000e+00	0.000000000	0.00000000
5	SOM	0	0.02626642	0.00750469	0.01876173	0.8362687	0.0001836137	0.01373337	0.000000000	0.000000000	...	0	0	0	0	0.05491699	0.04980843	0.005108557	6.04273e-05	0.000301143	0.02304167
6	GL2	0	0.44465291	0.26266417	0.18198874	0.7556003	0.0003307304	0.11095804	0.000000000	0.000000000	...	0	0	0	0	0.00000000	0.00000000	0.000000000	0.00000e+00	0.000000000	0.00000000

head(el2mat)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	LBD25	0	0.39186296	0.13062099	0.26124197	0.9472756	0.0002522762	0.10988854	0.003787879	0.001893939	...	0	0	0	0	0.00000000	0.000000000	0.00000000	0	0.0000000000	0.000000000
2	LAF1	0	0.10920771	0.05995717	0.04925054	0.9459935	0.0002347686	0.05236007	0.000000000	0.000000000	...	0	0	0	0	0.00000000	0.000000000	0.00000000	0	0.0000000000	0.000000000
3	AT2G42660	0	0.10706638	0.07494647	0.03211991	0.9388895	0.0001990948	0.05081013	0.000000000	0.000000000	...	0	0	0	0	0.00000000	0.000000000	0.00000000	0	0.0000000000	0.000000000
4	MEA	0	0.26338330	0.13276231	0.13062099	0.9351904	0.0002419182	0.08043440	0.005681818	0.003787879	...	0	0	0	0	0.00000000	0.000000000	0.00000000	0	0.0000000000	0.000000000
5	IAA1	0	0.12633833	0.08565310	0.04068522	0.9334580	0.0002400805	0.05946827	0.000000000	0.000000000	...	0	0	0	0	0.01190476	0.001322751	0.01058201	0	0.0001654403	0.006986066
6	AT3G21330	0	0.09421842	0.03426124	0.05995717	0.9331272	0.0002023272	0.04141264	0.000000000	0.000000000	...	0	0	0	0	0.00000000	0.000000000	0.00000000	0	0.0000000000	0.000000000

stem2pro <- as.data.frame(cbind(stem2pro[,1],apply(stem2pro[,3:62],2,min_max_normalize)))
pro2trans <- as.data.frame(cbind(pro2trans[,1],apply(pro2trans[,3:62],2,min_max_normalize)))
trans2el <- as.data.frame(cbind(trans2el[,1],apply(trans2el[,3:62],2,min_max_normalize)))
el2el <- as.data.frame(cbind(el2el[,1],apply(el2el[,3:62],2,min_max_normalize)))
el2mat <- as.data.frame(cbind(el2mat[,1],apply(el2mat[,3:62],2,min_max_normalize)))

dat <- stem2pro %>%
full_join(pro2trans, by = "V1") %>%
full_join(trans2el, by = "V1") %>%
full_join(el2el, by = "V1") %>%
full_join(el2mat, by = "V1")

dat[is.na(dat)] <- 0

n <- c('atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality',
       'tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality',
       'lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality',
       'cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality',
       'end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality',
       'per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality',
       'pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality',
       'xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality',
       'phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality',
       'col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality')

colnames(dat) <- c("TF",gsub("$","_1",n), gsub("$","_2",n),gsub("$","_3",n),gsub("$","_4",n),gsub("$","_5",n))

GeneID <- wanted_TFs$GeneID[match(dat$TF, wanted_TFs$Name)]

dat <- cbind(GeneID, dat)

head(dat)

A data.frame: 6 x 302

	GeneID	TF	atri_degree_centrality_1	atri_out_centrality_1	atri_in_centrality_1	atri_betweenness_centrality_1	atri_closeness_centrality_1	atri_eigenvector_centrality_1	tri_degree_centrality_1	tri_out_centrality_1	...	phl_in_centrality_5	phl_betweenness_centrality_5	phl_closeness_centrality_5	phl_eigenvector_centrality_5	col_degree_centrality_5	col_out_centrality_5	col_in_centrality_5	col_betweenness_centrality_5	col_closeness_centrality_5	col_eigenvector_centrality_5
	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	...	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>
1	AT3G20840	PLT1	0.915137614678899	1	0.0790960451977401	1	1	1	0	0	...	0	0	0	0	0.00345542501727712	0.00563380281690141	0.00135685210312076	0	0.342836498437367	0.0261636689560327
2	AT4G00238	AT4G00238	0.527522935779816	0.23989218328841	0.398305084745763	0.360760982033675	0.731273755803759	0.782437777695549	0.183606557377049	0.4	...	0	0	0	0	0	0	0	0	0	0
3	AT5G14750	WER	1	0.221024258760108	1	0.31690339572947	0.996288835905153	0.989673180827978	0.177049180327869	0.252173913043478	...	0	0	0	0	0	0	0	0	0	0
4	AT1G75710	BRON	0.809633027522936	0.549865229110512	0.42090395480226	0.305121813564105	0.852808377917617	0.903605582412613	0.380327868852459	0.991304347826087	...	0	0	0	0	0.0400829302004147	0.0746478873239437	0.0067842605156038	0.000151255419985883	0.739388969226133	0.236949389676998
5	AT3G44750	HDA3	0.972477064220184	0.296495956873315	0.887005649717514	0.279324616687047	0.944616443589609	0.965231788297498	0.521311475409836	0.565217391304348	...	0	0	0	0	0	0	0	0	0	0
6	AT4G23750	CRF2	0.740825688073395	0.21832884097035	0.68361581920904	0.248541999811871	0.71928820893187	0.910089474841417	0.2	0.365217391304348	...	0.00420168067226891	0	0.490717199326431	0.0731345247937308	0	0	0	0	0	0

dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric)))
dat$celltype_specificity <- min_max_normalize(apply(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric),1,numz))
dat$weighted_score <- dat$combined_score + dat$celltype_specificity
dat <- dat %>% arrange(desc(weighted_score))

head(dat)

A data.frame: 6 x 305

	GeneID	TF	atri_degree_centrality_1	atri_out_centrality_1	atri_in_centrality_1	atri_betweenness_centrality_1	atri_closeness_centrality_1	atri_eigenvector_centrality_1	tri_degree_centrality_1	tri_out_centrality_1	...	phl_eigenvector_centrality_5	col_degree_centrality_5	col_out_centrality_5	col_in_centrality_5	col_betweenness_centrality_5	col_closeness_centrality_5	col_eigenvector_centrality_5	combined_score	celltype_specificity	weighted_score
	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	...	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>
1	AT2G45050	GATA2	0.740825688073395	0.0727762803234501	0.836158192090396	0.164683472862384	0.898810177742123	0.923795434525079	1	0.0869565217391304	...	0	0	0	0	0	0	0	0.8952435	0.5884774	1.483721
2	AT5G24800	BZIP9	0	0	0	0	0	0	0	0	...	0.893177964282472	0	0	0	0	0	0	0.7462001	0.7037037	1.449904
3	AT3G43430	AT3G43430	0	0	0	0	0	0	0	0	...	0.476672700564731	0	0	0	0	0	0	0.8536400	0.5473251	1.400965
4	AT1G75710	BRON	0.809633027522936	0.549865229110512	0.42090395480226	0.305121813564105	0.852808377917617	0.903605582412613	0.380327868852459	0.991304347826087	...	0	0.0400829302004147	0.0746478873239437	0.0067842605156038	0.000151255419985883	0.739388969226133	0.236949389676998	0.7522895	0.6337449	1.386034
5	AT4G32880	HB-8	0	0	0	0	0	0	0	0	...	0.146825077321862	0	0	0	0	0	0	0.7371139	0.6337449	1.370859
6	AT1G54690	GAMMA-H2AX	0.81651376146789	0.102425876010782	0.898305084745763	0.0855987207224156	0.924373626913912	0.951898241959019	0.849180327868852	0.0869565217391304	...	0.050925897863794	0	0	0	0	0	0	1.0000000	0.3621399	1.362140

write.csv(dat,"Raw_TF_GRN_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=FALSE)

dat_raw <- dat

GO terms (Prepare GS)

gene_list <- read.table('./gene_list_1108.csv', sep=",", header = TRUE)

exptf <- intersect(gene_list$features, wanted_TFs$GeneID)
length(exptf)

1495

sfun <- read.csv('string_functional_annotations.tsv', sep="\t", header=TRUE)
sann <- read.csv('string_protein_annotations.tsv', sep="\t", header=TRUE)

gsgo1 <- unique(sfun[grep('root|xylem|phloem|procambium|pericycle|vascular|vasculature|stele|tracheary|sieve|trichoblast|atrichoblast|epidermis|epidermal tissue|lateral root cap|root hair|trichome|cortex|endodermis|ground tissue|columella|quiescent center',sfun$term.description, ignore.case=TRUE),]$X.node)
gsgo2 <- unique(sann[grep('root|xylem|phloem|procambium|pericycle|vascular|vasculature|stele|tracheary|sieve|trichoblast|atrichoblast|epidermis|epidermal tissue|lateral root cap|root hair|trichome|cortex|endodermis|ground tissue|columella|quiescent center',sann$domain_summary_url, ignore.case=TRUE),]$X.node)
gsgo <- sort(unique(c(gsgo1, gsgo2)))

#write.csv(data.frame(GeneID=gsgo),"./Gold_Standard_Root_TF_StringDB.csv", quote=FALSE, row.names=FALSE)

length(gsgo)
gsgo

209

1. 'A0A1I9LNX5'
2. 'AGL12'
3. 'AGL16'
4. 'AHK2'
5. 'AHK3'
6. 'AHK4'
7. 'AIL5'
8. 'AL6'
9. 'ANL2'
10. 'APL'
11. 'ARF10'
12. 'ARF16'
13. 'ARF17'
14. 'ARF19'
15. 'ARF7'
16. 'ARR1'
17. 'ARR10'
18. 'ARR12'
19. 'ARR2'
20. 'ATHB-13'
21. 'ATHB-15'
22. 'ATHB-23'
23. 'ATHB-8'
24. 'ATHB-X'
25. 'ATX1-2'
26. 'BEH1'
27. 'BHLH12'
28. 'BHLH155'
29. 'BHLH2'
30. 'BHLH32'
31. 'BHLH48'
32. 'BHLH54'
33. 'BHLH66'
34. 'BHLH69'
35. 'BHLH82'
36. 'BHLH83'
37. 'BHLH85'
38. 'BHLH86'
39. 'BIB'
40. 'BRM'
41. 'BZIP19'
42. 'BZIP23'
43. 'BZR2'
44. 'CHC1-2'
45. 'CHR12'
46. 'CHR23'
47. 'CPC'
48. 'CRF2'
49. 'DAR2'
50. 'DOF1.1'
51. 'DOF2.2'
52. 'DOF2.4'
53. 'DOF3.2'
54. 'DOF3.6'
55. 'DOF5.1'
56. 'DOF5.3'
57. 'DOF5.6'
58. 'DOT2'
59. 'DPB'
60. 'E2FB'
61. 'EFM'
62. 'ERF055'
63. 'ERF104'
64. 'ET2'
65. 'ETC1'
66. 'ETR1'
67. 'F13K3.12'
68. 'F24O1.3'
69. 'F27L4.4'
70. 'F2I11.230'
71. 'FEZ'
72. 'GAI'
73. 'GALT2'
74. 'GATA12'
75. 'GATA23'
76. 'GIF1'
77. 'GIS3'
78. 'GL2'
79. 'GL3'
80. 'GRF2-2'
81. 'GRF3-2'
82. 'GTL1'
83. 'HAG1'
84. 'HAT2'
85. 'HAT4'
86. 'HDG11'
87. 'HDG12'
88. 'HDG2'
89. 'HDT1'
90. 'HHO1'
91. 'HHO2'
92. 'HHO3'
93. 'HHO6'
94. 'HRS1'
95. 'HSFA4C'
96. 'IAA12'
97. 'IAA14'
98. 'IAA20'
99. 'IAA28'
100. 'IAA3'
101. 'IAA31'
102. 'IAA8'
103. 'IDD4'
104. 'IDD6'
105. 'IWS1'
106. 'JKD'
107. 'JMJ14'
108. 'JMJ25'
109. 'JMJ30'
110. 'JMJ32'
111. 'K15E6.5'
112. 'KAN1'
113. 'KNAT7'
114. 'KUA1'
115. 'LBD15'
116. 'LBD16'
117. 'LBD18'
118. 'LBD29'
119. 'LBD3'
120. 'LBD30'
121. 'LBD4'
122. 'LDL1'
123. 'LEP'
124. 'LHW'
125. 'LRP1'
126. 'MAMYB'
127. 'MGP'
128. 'MIF1'
129. 'MTERF9'
130. 'MYB12'
131. 'MYB124'
132. 'MYB20'
133. 'MYB23'
134. 'MYB32'
135. 'MYB36'
136. 'MYB43'
137. 'MYB46'
138. 'MYB52'
139. 'MYB60'
140. 'MYB61'
141. 'MYB7'
142. 'MYB83'
143. 'MYB85'
144. 'MYB86'
145. 'MYB88'
146. 'MYC3'
147. 'MYC4'
148. 'NAA10'
149. 'NAC003'
150. 'NAC005'
151. 'NAC007'
152. 'NAC010'
153. 'NAC026'
154. 'NAC030'
155. 'NAC037'
156. 'NAC043'
157. 'NAC045'
158. 'NAC056'
159. 'NAC075'
160. 'NAC076'
161. 'NAC083'
162. 'NAC084'
163. 'NAC086'
164. 'NAC101'
165. 'NAC104'
166. 'NAC105'
167. 'NAC92'
168. 'NUC'
169. 'PKL'
170. 'PLT1'
171. 'PLT2'
172. 'PRE3'
173. 'Q1G3Q4_ARATH'
174. 'RAP2-11'
175. 'RAP2-12'
176. 'RAV1'
177. 'RBR1'
178. 'REF6'
179. 'REV'
180. 'RGA'
181. 'SCL22'
182. 'SCL27'
183. 'SCL6'
184. 'SCR'
185. 'SCRM'
186. 'SHR'
187. 'SMB'
188. 'SOG1'
189. 'T15F16.14'
190. 'T2I1.110'
191. 'TCP14'
192. 'TCP15'
193. 'TCP9'
194. 'TCX2'
195. 'TCX3'
196. 'TRY'
197. 'TSO1'
198. 'TTG1'
199. 'UPB1'
200. 'WER'
201. 'WIP2-2'
202. 'WLIM2B'
203. 'WOX14'
204. 'WRKY13'
205. 'WRKY44'
206. 'WRKY75'
207. 'ZFP5'
208. 'ZFP6'
209. 'ZFP8'

gsgo <- gsub(",.*$","",gsub("^.*ath:","",sann[match(gsgo, sann$X.node),]$other_names_and_aliases))
gsgo[which(gsgo=='831248')]='AT5G14000'
gsgo

1. 'AT3G53680'
2. 'AT1G71692'
3. 'AT3G57230'
4. 'AT5G35750'
5. 'AT1G27320'
6. 'AT2G01830'
7. 'AT5G57390'
8. 'AT2G02470'
9. 'AT4G00730'
10. 'AT1G79430'
11. 'AT2G28350'
12. 'AT4G30080'
13. 'AT1G77850'
14. 'AT1G19220'
15. 'AT5G20730'
16. 'AT3G16857'
17. 'AT4G31920'
18. 'AT2G25180'
19. 'AT4G16110'
20. 'AT1G69780'
21. 'AT1G52150'
22. 'AT1G26960'
23. 'AT4G32880'
24. 'AT1G70920'
25. 'AT2G31650'
26. 'AT3G50750'
27. 'AT4G00480'
28. 'AT2G31280'
29. 'AT1G63650'
30. 'AT3G25710'
31. 'AT2G42300'
32. 'AT1G27740'
33. 'AT2G24260'
34. 'AT4G30980'
35. 'AT5G58010'
36. 'AT1G66470'
37. 'AT4G33880'
38. 'AT5G37800'
39. 'AT3G45260'
40. 'AT2G46020'
41. 'AT4G35040'
42. 'AT2G16770'
43. 'AT1G19350'
44. 'AT5G14170'
45. 'AT3G06010'
46. 'AT5G19310'
47. 'AT2G46410'
48. 'AT4G23750'
49. 'AT2G39830'
50. 'AT1G07640'
51. 'AT2G28810'
52. 'AT2G37590'
53. 'AT3G45610'
54. 'AT3G55370'
55. 'AT5G02460'
56. 'AT5G60200'
57. 'AT5G62940'
58. 'AT5G16780'
59. 'AT5G03415'
60. 'AT5G22220'
61. 'AT2G03500'
62. 'AT1G36060'
63. 'AT5G61600'
64. 'AT5G56780'
65. 'AT1G01380'
66. 'AT1G66340'
67. 'AT2G36720'
68. 'AT1G62310'
69. 'AT2G23780'
70. 'AT5G11340'
71. 'AT1G26870'
72. 'AT1G14920'
73. 'AT4G21060'
74. 'AT5G25830'
75. 'AT5G26930'
76. 'AT5G28640'
77. 'AT1G68360'
78. 'AT1G79840'
79. 'AT5G41315'
80. 'AT4G37740'
81. 'AT2G36400'
82. 'AT1G33240'
83. 'AT3G54610'
84. 'AT5G47370'
85. 'AT4G16780'
86. 'AT1G73360'
87. 'AT1G17920'
88. 'AT1G05230'
89. 'AT3G44750'
90. 'AT3G25790'
91. 'AT1G68670'
92. 'AT1G25550'
93. 'AT1G49560'
94. 'AT1G13300'
95. 'AT5G45710'
96. 'AT1G04550'
97. 'AT4G14550'
98. 'AT2G46990'
99. 'AT5G25890'
100. 'AT1G04240'
101. 'AT3G17600'
102. 'AT2G22670'
103. 'AT2G02080'
104. 'AT1G14580'
105. 'AT1G32130'
106. 'AT5G03150'
107. 'AT4G20400'
108. 'AT3G07610'
109. 'AT3G20810'
110. 'AT3G45880'
111. 'AT5G38840'
112. 'AT5G16560'
113. 'AT1G62990'
114. 'AT5G47390'
115. 'AT2G40470'
116. 'AT2G42430'
117. 'AT2G45420'
118. 'AT3G58190'
119. 'AT1G16530'
120. 'AT4G00220'
121. 'AT1G31320'
122. 'AT1G62830'
123. 'AT5G13910'
124. 'AT2G27230'
125. 'AT5G12330'
126. 'AT5G45420'
127. 'AT1G03840'
128. 'AT1G74660'
129. 'AT5G55580'
130. 'AT2G47460'
131. 'AT1G14350'
132. 'AT1G66230'
133. 'AT5G40330'
134. 'AT4G34990'
135. 'AT5G57620'
136. 'AT5G16600'
137. 'AT5G12870'
138. 'AT1G17950'
139. 'AT1G08810'
140. 'AT1G09540'
141. 'AT2G16720'
142. 'AT3G08500'
143. 'AT4G22680'
144. 'AT5G26660'
145. 'AT2G02820'
146. 'AT5G46760'
147. 'AT4G17880'
148. 'AT5G13780'
149. 'AT1G02220'
150. 'AT1G02250'
151. 'AT1G12260'
152. 'AT1G28470'
153. 'AT1G62700'
154. 'AT1G71930'
155. 'AT2G18060'
156. 'AT2G46770'
157. 'AT3G03200'
158. 'AT3G15510'
159. 'AT4G29230'
160. 'AT4G36160'
161. 'AT5G13180'
162. 'AT5G14000'
163. 'AT5G17260'
164. 'AT5G62380'
165. 'AT5G64530'
166. 'AT5G66300'
167. 'AT5G39610'
168. 'AT5G44160'
169. 'AT2G25170'
170. 'AT3G20840'
171. 'AT1G51190'
172. 'AT1G74500'
173. 'AT2G12646'
174. 'AT5G19790'
175. 'AT1G53910'
176. 'AT1G13260'
177. 'AT3G12280'
178. 'AT3G48430'
179. 'AT5G60690'
180. 'AT2G01570'
181. 'AT3G60630'
182. 'AT2G45160'
183. 'AT4G00150'
184. 'AT3G54220'
185. 'AT3G26744'
186. 'AT4G37650'
187. 'AT1G79580'
188. 'AT1G25580'
189. 'AT4G08455'
190. 'AT5G07400'
191. 'AT3G47620'
192. 'AT1G69690'
193. 'AT2G45680'
194. 'AT4G14770'
195. 'AT3G22760'
196. 'AT5G53200'
197. 'AT3G22780'
198. 'AT5G24520'
199. 'AT2G47270'
200. 'AT5G14750'
201. 'AT3G57670'
202. 'AT3G55770'
203. 'AT1G20700'
204. 'AT4G39410'
205. 'AT2G37260'
206. 'AT5G13080'
207. 'AT1G10480'
208. 'AT1G67030'
209. 'AT2G41940'

sann[which(sann$X.node=="HAT7"),]

A data.frame: 1 x 5

	X.node	identifier	domain_summary_url	annotation	other_names_and_aliases
	<chr>	<chr>	<chr>	<chr>	<chr>
668	HAT7	3702.Q00466	Homeobox-leucine zipper protein HAT7; Probable transcription factor.	https://smart.embl.de/smart/DDvec.cgi?smart=314:HOX(113|174)+	831367,AT5G15150,ATHB-3,At5g15150,F8M21_40,HAT7,HAT7_ARATH,HB-3,HD-ZIP protein 7,HD-ZIP protein ATHB-3,Homeobox 3,Homeobox-leucine zipper protein,Homeobox-leucine zipper protein HAT7,Homeodomain transcription factor ATHB-3,Homeodomain-leucine zipper protein HAT7,NM_121519.3,NP_568309,NP_568309.2,Q00466,Q0WNS2,Q9LXG6,ath:AT5G15150

sann[grep("831248",sann$other_names_and_aliases, ignore.case = TRUE),]

A data.frame: 1 x 5

	X.node	identifier	domain_summary_url	annotation	other_names_and_aliases
	<chr>	<chr>	<chr>	<chr>	<chr>
980	NAC084	3702.A0A1P8BAC2	NAC domain containing protein 84.	https://smart.embl.de/smart/DDvec.cgi?smart=262:Pfam_NAM(16|196)+	831248,A0A1P8BAC2,A0A1P8BAC2_ARATH,At5g14000,MAC12.3,MAC12_3,NAC domain containing protein 84,NAC084,NM_001343305.1,NP_001330278.1,anac084

R50

r50 <- 105
numz <- function(x){
    sum(x==0)/length(x)
}

GeneSys

genesys <- dat_genesys

run_r50_genesys <- function(x){
    genesys$combined_score <- min_max_normalize(rowSums(apply(genesys[,grep(x,colnames(genesys))],2,as.numeric)))
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## all cell type : in centrality + celltype & dev stage specificity
#dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("in_centrality",colnames(dat))],2,as.numeric)))
#dat$celltype_specificity <- min_max_normalize(apply(apply(dat[,grep("in_centrality",colnames(dat))],2,as.numeric),1,numz))
#dat$weighted_score <- dat$combined_score + dat$celltype_specificity
#dat <- dat %>% arrange(desc(weighted_score))

#count <- 0
#for (i in seq(nrow(dat))){
#    if (dat$GeneID[i] %in% gsgo){
#        count <- count +1
#        if (count == r50){
#            print(i)
#            break
#        }
#    }
#}

Raw

raw <- dat_raw

run_r50_raw <- function(x){
    raw$combined_score <- min_max_normalize(rowSums(apply(raw[,grep(x,colnames(raw))],2,as.numeric)))
    raw <- raw %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(raw))){
        if (raw$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

CellOracle

prepros <- function(x){
    dat <- read.csv(x)
    #dat <- dat %>% filter(role=="Connector Hub") 
    dat <- dat[,c(1, grep("centrality",colnames(dat)), 22)]
    return(dat)
}

atri <- prepros("../celloracle/atrichoblast_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
tri <- prepros("../celloracle/trichoblast_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
lrc <- prepros("../celloracle/lrc_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
cor <- prepros("../celloracle/cortex_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
end <- prepros("../celloracle/endodermis_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
per <- prepros("../celloracle/pericycle_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
pro <- prepros("../celloracle/procambium_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
xyl <- prepros("../celloracle/xylem_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
phl <- prepros("../celloracle/phloem_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
col <- prepros("../celloracle/columella_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")

dat <- rbind(atri, tri, lrc, cor, end, per, pro, xyl, phl, col)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

head(dat)

A data.frame: 6 x 7

	GeneID	degree_centrality	in_centrality	out_centrality	betweenness_centrality	closeness_centrality	eigenvector_centrality
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	AT1G01010	0.0849393998	0.065317964	0.081735954	2.592974e-02	0.3643692412	0.068339145
2	AT1G01030	0.0009542021	0.000000000	0.004447568	0.000000e+00	0.0003449773	0.003632920
3	AT1G01260	0.0233438132	0.000000000	0.033565069	0.000000e+00	0.0872081085	0.016136854
4	AT1G01350	0.0293468347	0.134378356	0.000000000	0.000000e+00	0.0000000000	0.017764785
5	AT1G01380	0.0035614661	0.005492245	0.004573149	7.240465e-05	0.0852853904	0.003752290
6	AT1G01640	0.0060441842	0.023215800	0.000000000	0.000000e+00	0.0000000000	0.006672674

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## all cell type : degree centrality + out centrality + in centrality + betweenness centrality + closeness + eigenvector
#dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric)))
#dat <- dat %>% arrange(desc(combined_score))

#count <- 0
#for (i in seq(nrow(dat))){
#    if (dat$GeneID[i] %in% gsgo){
#        count <- count +1
#        if (count == r50){
#            print(i)
#            break
#        }
#    }
#}

#dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("betweenness_centrality|celltype_specificity",colnames(dat))],2,as.numeric)))
#dat <- dat %>% arrange(desc(combined_score))

#count <- 0
#for (i in seq(nrow(dat))){
#    if (dat$GeneID[i] %in% gsgo){
#        count <- count +1
#        if (count == r50){
#            print(i)
#            break
#        }
#    }
#}

DE (Root_Atlas)

de <- read.csv("Root_Atlas_DE_Gene_List.csv")

de <- de %>% arrange(pct.diff_rank) %>% arrange(avg_diff_rank)%>% arrange(myAUC_rank)%>% arrange(combined_rank)

## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]

head(de)

A data.frame: 6 x 14

	myAUC	avg_diff	power	pct.1	pct.2	celltype	pseudotime.bin	gene.ID	gene.name	pct.diff	pct.diff_rank	avg_diff_rank	myAUC_rank	combined_rank
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>	<chr>	<chr>	<dbl>	<int>	<int>	<int>	<int>
13	0.901	1.985825	0.802	0.959	0.287	Endodermis	T4	AT5G13910	LEP	0.672	2	4	1	1
40	0.934	2.820810	0.868	0.900	0.155	Procambium	T0	AT1G54690	HTA3	0.745	2	35	2	1
51	0.841	2.351595	0.682	0.881	0.297	Procambium	T1	AT1G25560	TEM1	0.584	1	11	3	1
58	0.979	3.799126	0.958	1.000	0.046	Xylem	T1	AT4G22680	MYB85	0.954	1	7	4	1
63	0.945	4.666402	0.890	0.765	0.054	Phloem	T2	AT3G60530	GATA4	0.711	35	1	5	1
66	0.871	2.623028	0.742	0.877	0.242	Procambium	T2	AT1G66600	WRKY63	0.635	1	5	5	1

dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")

head(dat)

A tibble: 6 x 5

GeneID	combined_rank	myAUC_rank	pct.diff_rank	avg_diff_rank
<chr>	<dbl>	<dbl>	<dbl>	<dbl>
AT1G66600	6.500000	19.50000	23.00000	11.50000
AT5G58010	8.333333	9.00000	7.00000	16.00000
AT1G13600	9.500000	42.00000	34.50000	19.50000
AT4G37260	12.333333	10.33333	20.33333	41.66667
AT1G26680	13.000000	73.00000	37.00000	78.00000
AT1G61660	13.000000	19.66667	17.33333	38.33333

de <- dat

run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

Plot r50 with ranked by different centrality

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","Raw degree centrality", "Raw out centrality", "Raw in centrality", "Raw betweenness centrality", 
                 "Raw closeness centrality", "Raw eigenvector centrality", "CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_raw('degree_centrality'),run_r50_raw('out_centrality'),run_r50_raw('in_centrality'),
                 run_r50_raw('betweenness_centrality'),run_r50_raw('closeness_centrality'),run_r50_raw('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 run_r50_de('myAUC_rank'),run_r50_de('pct.diff_rank'),run_r50_de('avg_diff_rank'), mean(R50_permutation)))

toplt

A data.frame: 22 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	386.000
GeneSys out centrality	404.000
GeneSys in centrality	371.000
GeneSys betweenness centrality	367.000
GeneSys closeness centrality	735.000
GeneSys eigenvector centrality	461.000
Raw degree centrality	411.000
Raw out centrality	475.000
Raw in centrality	387.000
Raw betweenness centrality	446.000
Raw closeness centrality	879.000
Raw eigenvector centrality	534.000
CellOracle degree centrality	479.000
CellOracle out centrality	469.000
CellOracle in centrality	461.000
CellOracle betweenness centrality	413.000
CellOracle closeness centrality	505.000
CellOracle eigenvector centrality	488.000
DE myAUC rank	484.000
DE pct diff rank	495.000
DE avg diff rank	472.000
Expressed TFs permutation	748.623

options(repr.plot.width=10, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6), rep("Raw",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("Expressed TFs permutation", "DE", "CellOracle", "Raw", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=8, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.4)+ 
  labs(title="TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

R50 for Tissue specific TFs

Stele (xylem, phloem, procambium)

gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,5]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID

1. 'APL'
2. 'ATHB-8'
3. 'BHLH48'
4. 'DAR2'
5. 'DOF5.3'
6. 'DOF5.6'
7. 'DOT2'
8. 'ERF104'
9. 'ET2'
10. 'GATA12'
11. 'KAN1'
12. 'KNAT7'
13. 'LBD15'
14. 'LBD18'
15. 'LBD30'
16. 'LBD4'
17. 'LHW'
18. 'MYB20'
19. 'MYB32'
20. 'MYB43'
21. 'MYB46'
22. 'MYB52'
23. 'MYB61'
24. 'MYB7'
25. 'MYB83'
26. 'MYB85'
27. 'NAC005'
28. 'NAC007'
29. 'NAC010'
30. 'NAC026'
31. 'NAC030'
32. 'NAC037'
33. 'NAC043'
34. 'NAC045'
35. 'NAC075'
36. 'NAC076'
37. 'NAC083'
38. 'NAC086'
39. 'NAC101'
40. 'NAC104'
41. 'NAC105'
42. 'REV'
43. 'WOX14'

length(gsgo)

43

r50 <- 22

## Define genesys
run_r50_genesys <- function(x){
    genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('xyl_',x,'|phl_',x,'|pro_',x),colnames(genesys))],2,as.numeric)))
    genesys$combined_score <- genesys$ct_score
    
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define raw
run_r50_raw <- function(x){
    raw$ct_score <- min_max_normalize(rowSums(apply(raw[,grep(paste0('xyl_',x,'|phl_',x,'|pro_',x),colnames(raw))],2,as.numeric)))
    raw$combined_score <- raw$ct_score
    
    raw <- raw %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(raw))){
        if (raw$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define celloracle
dat <- rbind(pro, xyl, phl)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define DE
de <- read.csv("Root_Atlas_DE_Gene_List.csv")
de <- de %>% filter(celltype=="Xylem"|celltype=="Phloem"|celltype=="Procambium")
## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]
dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")
de <- dat
run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","Raw degree centrality", "Raw out centrality", "Raw in centrality", "Raw betweenness centrality", 
                 "Raw closeness centrality", "Raw eigenvector centrality", "CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_raw('degree_centrality'),run_r50_raw('out_centrality'),run_r50_raw('in_centrality'),
                 run_r50_raw('betweenness_centrality'),run_r50_raw('closeness_centrality'),run_r50_raw('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 run_r50_de('myAUC_rank'),run_r50_de('pct.diff_rank'),run_r50_de('avg_diff_rank'), mean(R50_permutation)))

toplt

A data.frame: 22 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	99.000
GeneSys out centrality	173.000
GeneSys in centrality	95.000
GeneSys betweenness centrality	117.000
GeneSys closeness centrality	583.000
GeneSys eigenvector centrality	178.000
Raw degree centrality	128.000
Raw out centrality	211.000
Raw in centrality	101.000
Raw betweenness centrality	171.000
Raw closeness centrality	847.000
Raw eigenvector centrality	212.000
CellOracle degree centrality	288.000
CellOracle out centrality	255.000
CellOracle in centrality	190.000
CellOracle betweenness centrality	154.000
CellOracle closeness centrality	337.000
CellOracle eigenvector centrality	252.000
DE myAUC rank	275.000
DE pct diff rank	302.000
DE avg diff rank	245.000
Expressed TFs permutation	747.726

options(repr.plot.width=10, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="Stele-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6), rep("Raw",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("Expressed TFs permutation", "DE", "CellOracle", "Raw", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=8, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.4)+ 
  labs(title="Stele-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

Epidermis

gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,6]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID

1. 'AGL16'
2. 'AL6'
3. 'ANL2'
4. 'BHLH12'
5. 'BHLH2'
6. 'BHLH32'
7. 'BHLH54'
8. 'BHLH66'
9. 'BHLH69'
10. 'BHLH82'
11. 'BHLH83'
12. 'BHLH85'
13. 'BHLH86'
14. 'CPC'
15. 'E2FB'
16. 'ETC1'
17. 'GALT2'
18. 'GIS3'
19. 'GL2'
20. 'GL3'
21. 'GTL1'
22. 'HDG11'
23. 'HDG12'
24. 'HDG2'
25. 'JMJ25'
26. 'MAMYB'
27. 'MYB124'
28. 'MYB23'
29. 'MYB86'
30. 'MYB88'
31. 'MYC3'
32. 'MYC4'
33. 'RBR1'
34. 'SCL22'
35. 'SCL27'
36. 'SCL6'
37. 'SCRM'
38. 'TCX2'
39. 'TCX3'
40. 'TRY'
41. 'TSO1'
42. 'TTG1'
43. 'WER'
44. 'WRKY44'
45. 'WRKY75'
46. 'ZFP5'
47. 'ZFP6'
48. 'ZFP8'

length(gsgo)
r50 <- 24

48

## Define genesys
run_r50_genesys <- function(x){
    genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('atri_',x,'|tri_',x,'|lrc_',x),colnames(genesys))],2,as.numeric)))
    genesys$combined_score <- genesys$ct_score
    
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define raw
run_r50_raw <- function(x){
    raw$ct_score <- min_max_normalize(rowSums(apply(raw[,grep(paste0('atri_',x,'|tri_',x,'|lrc_',x),colnames(raw))],2,as.numeric)))
    raw$combined_score <- raw$ct_score
    
    raw <- raw %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(raw))){
        if (raw$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define celloracle
dat <- rbind(atri, tri, lrc)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define DE
de <- read.csv("Root_Atlas_DE_Gene_List.csv")
de <- de %>% filter(celltype=="Atrichoblast"|celltype=="Trichoblast"|celltype=="Lateral Root Cap")
## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]
dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")
de <- dat
run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","Raw degree centrality", "Raw out centrality", "Raw in centrality", "Raw betweenness centrality", 
                 "Raw closeness centrality", "Raw eigenvector centrality", "CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_raw('degree_centrality'),run_r50_raw('out_centrality'),run_r50_raw('in_centrality'),
                 run_r50_raw('betweenness_centrality'),run_r50_raw('closeness_centrality'),run_r50_raw('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 run_r50_de('myAUC_rank'),run_r50_de('pct.diff_rank'),run_r50_de('avg_diff_rank'), 999,999,999,mean(R50_permutation)))

toplt

A data.frame: 22 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	284.000
GeneSys out centrality	309.000
GeneSys in centrality	299.000
GeneSys betweenness centrality	284.000
GeneSys closeness centrality	324.000
GeneSys eigenvector centrality	254.000
Raw degree centrality	289.000
Raw out centrality	267.000
Raw in centrality	294.000
Raw betweenness centrality	383.000
Raw closeness centrality	560.000
Raw eigenvector centrality	325.000
CellOracle degree centrality	391.000
CellOracle out centrality	365.000
CellOracle in centrality	455.000
CellOracle betweenness centrality	304.000
CellOracle closeness centrality	351.000
CellOracle eigenvector centrality	392.000
DE myAUC rank	999.000
DE pct diff rank	999.000
DE avg diff rank	999.000
Expressed TFs permutation	727.992

options(repr.plot.width=12, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="Epidermis-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6), rep("Raw",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("DE","Expressed TFs permutation", "CellOracle", "Raw", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=8, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.4)+ 
  labs(title="Epidermis-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

R50 for celltype specific

Xylem

gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,7]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID

1. 'APL'
2. 'ATHB-8'
3. 'BHLH48'
4. 'ET2'
5. 'KNAT7'
6. 'LBD18'
7. 'LBD30'
8. 'LHW'
9. 'MYB20'
10. 'MYB32'
11. 'MYB43'
12. 'MYB46'
13. 'MYB52'
14. 'MYB61'
15. 'MYB7'
16. 'MYB83'
17. 'MYB85'
18. 'NAC005'
19. 'NAC007'
20. 'NAC010'
21. 'NAC026'
22. 'NAC030'
23. 'NAC037'
24. 'NAC043'
25. 'NAC076'
26. 'NAC083'
27. 'NAC101'
28. 'NAC104'
29. 'NAC105'
30. 'REV'

length(gsgo)
r50 <- 15

30

## Define genesys
run_r50_genesys <- function(x){
    genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('xyl_',x),colnames(genesys))],2,as.numeric)))
    genesys$combined_score <- genesys$ct_score
    
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define genesys
run_r50_raw <- function(x){
    raw$ct_score <- min_max_normalize(rowSums(apply(raw[,grep(paste0('xyl_',x),colnames(raw))],2,as.numeric)))
    raw$combined_score <- raw$ct_score
    
    raw <- raw %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(raw))){
        if (raw$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define celloracle
dat <- rbind(xyl)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define DE
de <- read.csv("Root_Atlas_DE_Gene_List.csv")
de <- de %>% filter(celltype=="Xylem")
## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]
dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")
de <- dat
run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","Raw degree centrality", "Raw out centrality", "Raw in centrality", "Raw betweenness centrality", 
                 "Raw closeness centrality", "Raw eigenvector centrality", "CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_raw('degree_centrality'),run_r50_raw('out_centrality'),run_r50_raw('in_centrality'),
                 run_r50_raw('betweenness_centrality'),run_r50_raw('closeness_centrality'),run_r50_raw('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 run_r50_de('myAUC_rank'),run_r50_de('pct.diff_rank'),run_r50_de('avg_diff_rank'), mean(R50_permutation)))

toplt

A data.frame: 22 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	33.000
GeneSys out centrality	60.000
GeneSys in centrality	28.000
GeneSys betweenness centrality	74.000
GeneSys closeness centrality	49.000
GeneSys eigenvector centrality	53.000
Raw degree centrality	35.000
Raw out centrality	41.000
Raw in centrality	34.000
Raw betweenness centrality	92.000
Raw closeness centrality	53.000
Raw eigenvector centrality	36.000
CellOracle degree centrality	90.000
CellOracle out centrality	88.000
CellOracle in centrality	65.000
CellOracle betweenness centrality	50.000
CellOracle closeness centrality	135.000
CellOracle eigenvector centrality	62.000
DE myAUC rank	130.000
DE pct diff rank	133.000
DE avg diff rank	108.000
Expressed TFs permutation	723.582

options(repr.plot.width=12, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="Xylem-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6),rep("Raw",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("Expressed TFs permutation","DE", "CellOracle", "Raw", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=8, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.4)+ 
  labs(title="Xylem-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

Trichoblast

gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,8]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID

1. 'AL6'
2. 'ANL2'
3. 'BHLH12'
4. 'BHLH2'
5. 'BHLH32'
6. 'BHLH54'
7. 'BHLH66'
8. 'BHLH69'
9. 'BHLH82'
10. 'BHLH83'
11. 'BHLH85'
12. 'BHLH86'
13. 'CPC'
14. 'E2FB'
15. 'ETC1'
16. 'GALT2'
17. 'GIS3'
18. 'GL2'
19. 'GL3'
20. 'GTL1'
21. 'HDG11'
22. 'HDG12'
23. 'HDG2'
24. 'MAMYB'
25. 'MYB23'
26. 'MYB86'
27. 'RBR1'
28. 'SCL22'
29. 'SCL27'
30. 'SCL6'
31. 'TRY'
32. 'TTG1'
33. 'WER'
34. 'ZFP5'
35. 'ZFP6'
36. 'ZFP8'

length(gsgo)
r50 <- 18

36

## Define genesys
run_r50_genesys <- function(x){
    genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('^tri_',x),colnames(genesys))],2,as.numeric)))
    genesys$combined_score <- genesys$ct_score
    
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define raw
run_r50_raw <- function(x){
    raw$ct_score <- min_max_normalize(rowSums(apply(raw[,grep(paste0('^tri_',x),colnames(raw))],2,as.numeric)))
    raw$combined_score <- raw$ct_score
    
    raw <- raw %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(raw))){
        if (raw$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define celloracle
dat <- rbind(tri)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define DE
de <- read.csv("Root_Atlas_DE_Gene_List.csv")
de <- de %>% filter(celltype=="Trichoblast")
## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]
dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")
de <- dat
run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","Raw degree centrality", "Raw out centrality", "Raw in centrality", "Raw betweenness centrality", 
                 "Raw closeness centrality", "Raw eigenvector centrality", "CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_raw('degree_centrality'),run_r50_raw('out_centrality'),run_r50_raw('in_centrality'),
                 run_r50_raw('betweenness_centrality'),run_r50_raw('closeness_centrality'),run_r50_raw('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 run_r50_de('myAUC_rank'),run_r50_de('pct.diff_rank'),run_r50_de('avg_diff_rank'), 999,999,999,mean(R50_permutation)))

toplt

A data.frame: 22 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	189.000
GeneSys out centrality	217.000
GeneSys in centrality	212.000
GeneSys betweenness centrality	262.000
GeneSys closeness centrality	271.000
GeneSys eigenvector centrality	194.000
Raw degree centrality	385.000
Raw out centrality	326.000
Raw in centrality	411.000
Raw betweenness centrality	268.000
Raw closeness centrality	449.000
Raw eigenvector centrality	426.000
CellOracle degree centrality	508.000
CellOracle out centrality	476.000
CellOracle in centrality	425.000
CellOracle betweenness centrality	339.000
CellOracle closeness centrality	449.000
CellOracle eigenvector centrality	501.000
DE myAUC rank	999.000
DE pct diff rank	999.000
DE avg diff rank	999.000
Expressed TFs permutation	722.387

options(repr.plot.width=12, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="Trichoblast-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6), rep("Raw",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("DE","Expressed TFs permutation", "CellOracle", "Raw", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=8, repr.plot.height=5)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.4)+ 
  labs(title="Trichoblast-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

Plotting

dat <- genesys

plot_heatmap <- function(gene, centrality){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep(centrality,colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = TRUE, name = paste0(gene,"\n","weighted","\n","network","\n","centrality"),
            col = col_fun, column_title = paste0(str_split_i(centrality,"_",1),"\n",str_split_i(centrality,"_",2)), column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_heatmap2 <- function(gene, centrality){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep(centrality,colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = "out degree \n centrality", column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_heatmap3 <- function(gene, centrality){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep(centrality,colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1), name = paste0("unweighted","\n","network","\n","centrality"),
            col = col_fun, column_title = "in degree \n centrality", column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_all_centrality <- function(gene){
    options(repr.plot.width=10, repr.plot.height=6)
    plot_heatmap(gene,"betweenness_centrality") + plot_heatmap2(gene,"out_centrality") + plot_heatmap3(gene,"in_centrality")
}

plot_all_centrality("TTG1")

plot_all_centrality("CPC")

plot_all_centrality("SHR")

plot_all_centrality("SCR")

plot_all_centrality("BLJ")

plot_all_centrality("JKD")

plot_all_centrality("MYB36")

plot_all_centrality("RVN")

plot_all_centrality("MGP")

plot_all_centrality("NUC")

plot_all_centrality("WER")

## HAT7
plot_all_centrality("HAT7")

## GATA10
plot_all_centrality("GATA10")

## GATA11
plot_all_centrality("GATA11")

plot_all_centrality("AN3")

plot_all_centrality("GL2")

plot_all_centrality("LBD15")

plot_all_centrality <- function(gene){
    options(repr.plot.width=8, repr.plot.height=4)
    plot_heatmap(gene,"betweenness_centrality") + plot_heatmap2(gene,"out_centrality") + plot_heatmap3(gene,"in_centrality")
}

plot_all_centrality("SHR")

plot_all_centrality("WER")

Rank by betweenness centrality, out-degree centrality, in-degree centrality and degree centrality

plot_bc <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("betweenness_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_oc <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("out_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_ic <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("in_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_dc <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("degree_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_blank <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("betweenness_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('white',"white", "white"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, show_row_names = TRUE
       )
}

# Combine the top three centralies 
sub <- dat[,grep("betweenness_centrality|in_centrality|out_centrality",colnames(dat))]
sub <- as.data.frame(sapply(sub, as.numeric))
rownames(sub) <- dat$TF

head(sub)

A data.frame: 6 x 150

	atri_out_centrality_1	atri_in_centrality_1	atri_betweenness_centrality_1	tri_out_centrality_1	tri_in_centrality_1	tri_betweenness_centrality_1	lrc_out_centrality_1	lrc_in_centrality_1	lrc_betweenness_centrality_1	cor_out_centrality_1	...	pro_betweenness_centrality_5	xyl_out_centrality_5	xyl_in_centrality_5	xyl_betweenness_centrality_5	phl_out_centrality_5	phl_in_centrality_5	phl_betweenness_centrality_5	col_out_centrality_5	col_in_centrality_5	col_betweenness_centrality_5
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
BZIP9	0.0000000	0.0000000	0.000000	0.00000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.000000000	...	0.9210677	0	0	0	0.8440367	0.8463303	0.2032263	0.00000000	0.000000000	0
AT3G43430	0.0000000	0.0000000	0.000000	0.00000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.002673797	...	0.5052969	0	0	0	0.2350917	0.1089450	0.1105647	0.00000000	0.000000000	0
PLT1	0.2160494	0.5653710	0.963923	0.00000000	0.0000000	0.0000000	0.1568627	1.0000000	0.9868855	0.385026738	...	0.0000000	0	0	0	0.0000000	0.0000000	0.0000000	0.09975669	0.002207506	0
HAT7	0.0000000	0.0000000	0.000000	0.05179283	0.3275862	0.8317337	0.0000000	0.0000000	0.0000000	0.024064171	...	0.0000000	0	0	0	0.0000000	0.0000000	0.0000000	0.00000000	0.000000000	0
MYB36	0.0000000	0.0000000	0.000000	0.00000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.114973262	...	0.0000000	0	0	0	0.0000000	0.0000000	0.0000000	0.00000000	0.000000000	0
GATA2	0.7932099	0.5759717	0.804800	0.66533865	0.5905172	0.8920287	0.8591800	0.9802513	1.0000000	0.553475936	...	0.0000000	0	0	0	0.0000000	0.0000000	0.0000000	0.00000000	0.000000000	0

bc_rank <- data.frame(all=rowSums(sub),atri=rowSums(sub[,grep("^atri_",colnames(sub))]),tri=rowSums(sub[,grep("^tri_",colnames(sub))])
                     ,cor=rowSums(sub[,grep("^cor_",colnames(sub))]),end=rowSums(sub[,grep("^end_",colnames(sub))])
                     ,per=rowSums(sub[,grep("^per_",colnames(sub))]),pro=rowSums(sub[,grep("^pro_",colnames(sub))])
                     ,xyl=rowSums(sub[,grep("^xyl_",colnames(sub))]),phl=rowSums(sub[,grep("^phl_",colnames(sub))])
                     ,lrc=rowSums(sub[,grep("^lrc_",colnames(sub))]),col=rowSums(sub[,grep("^col_",colnames(sub))]))

bc_rank$GeneID <- wanted_TFs$GeneID[match(rownames(bc_rank),wanted_TFs$Name)]

head(bc_rank)

A data.frame: 6 x 12

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
BZIP9	31.42213	0.000000	0.000000	0.00000000	0.005736138	8.55316613	11.88088107	0.02003315	10.9623184	0.000000	0.000000	AT5G24800
AT3G43430	29.05965	0.000000	0.000000	0.00544388	0.029360833	9.20656526	11.24626128	1.83863137	6.7333880	0.000000	0.000000	AT3G43430
PLT1	23.97514	3.940608	0.000000	2.00953127	1.492057686	0.05699842	0.00000000	0.00000000	0.0000000	9.148008	7.327940	AT3G20840
HAT7	33.13977	6.200914	5.547329	8.04274907	5.877125010	1.24427942	0.00000000	0.00000000	0.0122843	4.284922	1.930171	AT5G15150
MYB36	20.57418	0.000000	0.000000	6.29093689	10.748544272	3.53469871	0.00000000	0.00000000	0.0000000	0.000000	0.000000	AT5G57620
GATA2	33.12273	6.623792	6.596557	2.46621779	2.051664424	0.80375905	0.03881894	0.09406244	0.1559529	12.103509	2.188393	AT2G45050

Atrichoblast

atri_rank <- bc_rank[which(bc_rank$atri*2 > bc_rank$all),]%>% arrange(desc(atri))
atri_rank$GeneName <- rownames(atri_rank)

atri_rank

A data.frame: 43 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
MYB23	16.31793255	8.20713964	3.311916916	1.435215442	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	3.216075778	0.147584780	AT5G40330	MYB23
GL2	12.59879540	7.07284877	3.606946457	0.121283719	0.126559384	0.000000000	0.000000000	0.000000000	0.000000000	1.671157066	0.000000000	AT1G79840	GL2
TTG2	9.80635891	6.93096555	2.295746399	0.043262180	0.031031246	0.000000000	0.000000000	0.000000000	0.000000000	0.505353540	0.000000000	AT2G37260	TTG2
MYB45	7.74054316	5.00001186	1.548317405	1.105903099	0.032240503	0.000000000	0.000000000	0.000000000	0.000000000	0.054070289	0.000000000	AT3G48920	MYB45
IAA14	8.69681235	4.86976717	2.350063082	0.057152540	0.000000000	0.167830167	0.486865950	0.168121035	0.006276239	0.549981601	0.040754561	AT4G14550	IAA14
WRKY45	5.28282187	4.52127277	0.662200395	0.028887457	0.005736138	0.004460176	0.000000000	0.000000000	0.000000000	0.045499150	0.014765782	AT3G01970	WRKY45
ARR6	6.83180069	3.82931392	1.604692837	0.145406277	0.091157087	0.142688889	0.192916368	0.206112299	0.068509068	0.263100941	0.287903003	AT5G62920	ARR6
CRF4	6.17032329	3.58350854	1.213204139	0.590897376	0.216926392	0.051316603	0.000000000	0.000000000	0.000000000	0.475292500	0.039177743	AT4G27950	CRF4
AT2G28710	3.60999396	3.21999351	0.304157050	0.000000000	0.007648184	0.000000000	0.000000000	0.000000000	0.000000000	0.078195215	0.000000000	AT2G28710	AT2G28710
NAC6	3.86309350	2.92448703	0.595094653	0.000000000	0.054940125	0.000000000	0.000000000	0.000000000	0.000000000	0.288571683	0.000000000	AT5G39610	NAC6
MYB50	5.35178146	2.75139760	0.503140568	0.059817338	0.000000000	0.000000000	0.137683480	1.872933038	0.026809435	0.000000000	0.000000000	AT1G57560	MYB50
FIT	4.32726936	2.73484046	0.720809085	0.000000000	0.028669678	0.000000000	0.000000000	0.000000000	0.000000000	0.767557913	0.075392220	AT2G28160	FIT
WRKY27	4.76095235	2.51398738	0.660387042	0.059208675	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	1.467623124	0.059746125	AT5G52830	WRKY27
HB17	4.33177254	2.47505819	1.259045691	0.000000000	0.002868069	0.000000000	0.007639326	0.587161264	0.000000000	0.000000000	0.000000000	AT2G01430	HB17
AT3G05860	2.75994367	1.88069000	0.866575597	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.012678080	0.000000000	AT3G05860	AT3G05860
OFP18	2.11012124	1.84270868	0.225334328	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.042078228	0.000000000	AT3G52540	OFP18
LBD25	2.55363820	1.61563577	0.025062145	0.743786235	0.012198036	0.119627597	0.037328418	0.000000000	0.000000000	0.000000000	0.000000000	AT3G27650	LBD25
NAC044	2.74052836	1.53785196	0.185048913	0.095316041	0.213034297	0.630820177	0.000000000	0.000000000	0.000000000	0.078456974	0.000000000	AT3G01600	NAC044
RMR1	1.99874239	1.45886013	0.043433252	0.080247076	0.115541829	0.032785470	0.134617232	0.026320342	0.075999734	0.000000000	0.030937316	AT5G66160	RMR1
NAC003	2.32762624	1.40760202	0.311770468	0.353506885	0.059890914	0.071963621	0.052664353	0.048429810	0.000000000	0.000000000	0.021798166	AT1G02220	NAC003
KAN	2.20987712	1.27353492	0.322695691	0.121476291	0.023721770	0.000000000	0.000000000	0.000000000	0.000000000	0.385566914	0.082881531	AT5G16560	KAN
AT5G22890	1.36913636	1.14378922	0.206470408	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.018876724	AT5G22890	AT5G22890
HB30	1.27288069	1.09275226	0.006587797	0.000000000	0.002868069	0.025097682	0.000000000	0.041624158	0.007747006	0.096203717	0.000000000	AT5G15210	HB30
WRKY47	1.65831084	1.07182034	0.212405427	0.120534320	0.253550750	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G01720	WRKY47
PTF1	1.69335729	1.01681850	0.130021628	0.245983102	0.255931767	0.000000000	0.008178273	0.000000000	0.036424023	0.000000000	0.000000000	AT3G02150	PTF1
AT2G18670	1.45742465	0.79056754	0.343466303	0.229416723	0.021959243	0.000000000	0.000000000	0.000000000	0.000000000	0.032673780	0.039341067	AT2G18670	AT2G18670
HB24	1.25568687	0.77531120	0.216848330	0.003880024	0.000000000	0.040533963	0.193332105	0.007524815	0.004456859	0.013799569	0.000000000	AT2G18350	HB24
AT4G31650	0.55219987	0.40261254	0.092706568	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.056880762	0.000000000	AT4G31650	AT4G31650
HSFB3	0.39912209	0.37453625	0.024585844	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G41690	HSFB3
GIS3	0.47235893	0.32971951	0.142639415	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G68360	GIS3
NAC069	0.53796818	0.29079612	0.098868360	0.000000000	0.002868069	0.000000000	0.000000000	0.000000000	0.000000000	0.042125364	0.103310267	AT4G01550	NAC069
MEA	0.29503532	0.25268757	0.007985591	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.034362168	0.000000000	AT1G02580	MEA
AT4G01350	0.38758173	0.23189368	0.100963743	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.048464513	0.006259796	AT4G01350	AT4G01350
NLP4	0.27856383	0.19051177	0.055025405	0.000000000	0.002868069	0.000000000	0.000000000	0.000000000	0.030158584	0.000000000	0.000000000	AT1G20640	NLP4
AT1G11490	0.21912705	0.16563057	0.045739637	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.007756839	0.000000000	AT1G11490	AT1G11490
PHE1	0.12111551	0.11318570	0.007929803	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G65330	PHE1
AT4G18110	0.07830247	0.07830247	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G18110	AT4G18110
AT1G14600	0.08088867	0.07228446	0.000000000	0.000000000	0.008604207	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G14600	AT1G14600
WRKY13	0.04582144	0.04582144	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G39410	WRKY13
LBD26	0.06567464	0.04082963	0.011156338	0.007722008	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.005966671	0.000000000	AT3G27940	LBD26
AT3G07260	0.07593283	0.03971993	0.000000000	0.000000000	0.036212897	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT3G07260	AT3G07260
AT4G38070	0.04134166	0.02628935	0.008360148	0.000000000	0.006692161	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G38070	AT4G38070
AT3G13840	0.01119403	0.01119403	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT3G13840	AT3G13840

options(repr.plot.width=6, repr.plot.height=4)
ggplot(atri_rank[1:10,], aes(x=reorder(GeneName, atri, decreasing = FALSE),  y=atri)) + geom_point(size=4)+ 
  labs(title="Atrichoblast-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(atri_rank,"Atrichoblast_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- atri_rank %>% rownames(.)

p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Atrichoblast ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Trichoblast

tri_rank <- bc_rank[which(bc_rank$tri*2 > bc_rank$all),]%>% arrange(desc(tri))
tri_rank$GeneName <- rownames(tri_rank)

tri_rank

A data.frame: 43 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
AT3G53370	10.703067482	1.809742147	8.309677470	0.000000000	0.000000000	0.162668647	0.05408138	0.325731713	0.041166121	0.000000000	0.000000000	AT3G53370	AT3G53370
LRL3	11.471484177	3.379461231	8.092022945	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G58010	LRL3
RHD6	10.231879434	3.043919316	7.187960117	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G66470	RHD6
AT4G09100	5.319501634	0.236489354	5.083012279	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G09100	AT4G09100
RSL4	5.167261070	0.110264729	5.056996342	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G27740	RSL4
RSL2	4.524441211	0.051378954	4.473062258	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G33880	RSL2
RAP2.11	3.210204477	0.000000000	3.197663118	0.000000000	0.000000000	0.000000000	0.00000000	0.012541359	0.000000000	0.000000000	0.000000000	AT5G19790	RAP2.11
AT4G39160	3.554318032	0.223674563	3.189048364	0.000000000	0.012428298	0.004384828	0.02017415	0.000000000	0.047321292	0.009035242	0.048251301	AT4G39160	AT4G39160
AT5G56200	3.181772719	0.000000000	3.181772719	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G56200	AT5G56200
AT2G37120	5.044905135	0.828925741	2.583107573	0.055263384	0.156456222	0.179113807	0.01628388	0.974156109	0.118228679	0.129591337	0.003778406	AT2G37120	AT2G37120
ESE3	2.683309001	0.119329105	2.206075220	0.205807146	0.142115650	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.009981880	AT5G25190	ESE3
WRKY61	4.259870565	2.109937239	2.149933326	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G18860	WRKY61
AT5G06800	2.866448697	1.077281362	1.737509465	0.000000000	0.012987961	0.005909557	0.00000000	0.000000000	0.032760352	0.000000000	0.000000000	AT5G06800	AT5G06800
WRKY70	2.616735517	0.362206667	1.543605842	0.000000000	0.000000000	0.073952273	0.36791832	0.018263683	0.003339541	0.130639555	0.116809634	AT3G56400	WRKY70
RL6	1.533961941	0.000000000	1.533961941	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G75250	RL6
WRKY72	2.631209313	0.397527208	1.330070167	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.717404861	0.186207077	AT5G15130	WRKY72
GL3	1.264223940	0.268798576	0.978997438	0.016427926	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G41315	GL3
AT1G61990	1.646543329	0.308764613	0.924735513	0.107070378	0.121099269	0.107694175	0.04423033	0.002508272	0.000000000	0.019692759	0.010748023	AT1G61990	AT1G61990
TAFII15	1.499091664	0.057474093	0.761413981	0.003880024	0.031248686	0.132966342	0.14326205	0.204642632	0.155406349	0.000000000	0.008797508	AT4G31720	TAFII15
AT2G05160	0.724767538	0.054812165	0.662710899	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.007244474	0.000000000	0.000000000	AT2G05160	AT2G05160
AT2G20030	0.700484313	0.073484201	0.627000112	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G20030	AT2G20030
AT5G04390	0.592985046	0.003731343	0.577765006	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.011488697	AT5G04390	AT5G04390
MC2	1.031987270	0.456245534	0.575741737	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G25110	MC2
WRKY42	1.095053422	0.181274511	0.565437156	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.248962871	0.099378883	AT4G04450	WRKY42
SUVR4	0.382088900	0.013690228	0.294552531	0.011583012	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.022498765	0.039764365	AT3G04380	SUVR4
AT3G22560	0.361986289	0.106681072	0.255305218	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT3G22560	AT3G22560
RAD54	0.280632949	0.000000000	0.155946722	0.032181848	0.000000000	0.011397451	0.00000000	0.000000000	0.000000000	0.069112527	0.011994401	AT3G19210	RAD54
MYB47	0.258138935	0.109314549	0.141176202	0.000000000	0.007648184	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G18710	MYB47
EIL2	0.138109082	0.000000000	0.138109082	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G21120	EIL2
AT2G14760	0.131975607	0.000000000	0.131975607	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G14760	AT2G14760
AGL87	0.139175318	0.033099943	0.106075375	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G22590	AGL87
AT2G17600	0.105079547	0.000000000	0.105079547	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G17600	AT2G17600
ASG3	0.153960813	0.018656716	0.103988574	0.000000000	0.002868069	0.002982303	0.02546515	0.000000000	0.000000000	0.000000000	0.000000000	AT2G44980	ASG3
AT1G02040	0.057532915	0.000000000	0.057532915	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G02040	AT1G02040
BPC5	0.067838909	0.013239977	0.054598932	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G38910	BPC5
SUVH2	0.088595628	0.000000000	0.048157148	0.000000000	0.002868069	0.002982303	0.02574518	0.000000000	0.008842931	0.000000000	0.000000000	AT2G33290	SUVH2
NAC005	0.032032046	0.000000000	0.032032046	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G02250	NAC005
IAA34	0.024804739	0.000000000	0.024804739	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G15050	IAA34
CIA2	0.038534594	0.010153558	0.024556945	0.000000000	0.003824092	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G57180	CIA2
AT2G24680	0.029624829	0.000000000	0.016915098	0.000000000	0.000000000	0.000000000	0.01270973	0.000000000	0.000000000	0.000000000	0.000000000	AT2G24680	AT2G24680
RR14	0.029976546	0.000000000	0.015544609	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.014431937	AT2G01760	RR14
AT2G28920	0.012650573	0.000000000	0.012650573	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G28920	AT2G28920
MYB97	0.009373312	0.000000000	0.009373312	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G26930	MYB97

options(repr.plot.width=6, repr.plot.height=4)
ggplot(tri_rank[1:10,], aes(x=reorder(GeneName, tri, decreasing = FALSE),  y=tri)) + geom_point(size=4)+ 
  labs(title="Trichoblast-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(tri_rank,"Trichoblast_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

## Top20 only
tf_rank <- tri_rank %>% rownames(.)
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Trichoblast ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Cortex

cor_rank <- bc_rank[which(bc_rank$cor*2 > bc_rank$all),]%>% arrange(desc(cor))
cor_rank$GeneName <- rownames(cor_rank)

cor_rank

A data.frame: 18 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
AT1G05710	7.83989003	0.07228446	0.000000000	5.77386624	1.973841857	0.01444529	0.005452182	0.0000000	0.000000000	0.000000000	0.000000000	AT1G05710	AT1G05710
AT1G62975	6.72777431	1.13322162	0.146830936	3.40415652	1.322609983	0.27190233	0.136499115	0.1189206	0.193633211	0.000000000	0.000000000	AT1G62975	AT1G62975
AT1G72210	4.37996569	0.00000000	0.000000000	3.26158461	0.916443668	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.201937410	AT1G72210	AT1G72210
AT2G38300	3.02146138	0.00000000	0.000000000	2.35652364	0.308759503	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.356178233	AT2G38300	AT2G38300
LAF1	2.65672644	0.07291432	0.000000000	2.16594894	0.417863168	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.000000000	AT4G25560	LAF1
MYB86	3.66808131	1.58670619	0.035736296	1.89230824	0.006692161	0.00000000	0.000000000	0.1034472	0.004497468	0.028711865	0.009981880	AT5G26660	MYB86
JAZ6	2.58639791	0.32191094	0.079950398	1.76647996	0.194759488	0.10574310	0.065125186	0.0000000	0.019982403	0.008591619	0.023854811	AT1G72450	JAZ6
AT2G42660	1.45982831	0.05077908	0.000000000	1.29597136	0.000000000	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.113077867	AT2G42660	AT2G42660
IDD4	1.73576688	0.00000000	0.000000000	1.28051964	0.455247239	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.000000000	AT2G02080	IDD4
LRP1	2.28649090	0.00000000	0.000000000	1.24876279	0.251133632	0.27895655	0.482865086	0.0000000	0.012253259	0.000000000	0.012519592	AT5G12330	LRP1
AT4G28030	2.08413938	0.28480728	0.443496572	1.06622232	0.257186184	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.032427031	AT4G28030	AT4G28030
AT1G64380	1.91747634	0.59839625	0.042775041	1.04414078	0.232164269	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.000000000	AT1G64380	AT1G64380
RVN	1.63173317	0.00000000	0.000000000	0.84336498	0.782052065	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.006316118	AT2G02070	RVN
GLK2	0.94299487	0.00000000	0.000000000	0.58189615	0.002868069	0.11657498	0.190199334	0.0000000	0.047677935	0.000000000	0.003778406	AT5G44190	GLK2
AT2G46810	0.20185238	0.00000000	0.003992795	0.11957719	0.041082783	0.00000000	0.011148486	0.0000000	0.000000000	0.000000000	0.026051124	AT2G46810	AT2G46810
AT3G18960	0.09413402	0.00000000	0.000000000	0.08787423	0.000000000	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.006259796	AT3G18960	AT3G18960
AT4G11400	0.03603606	0.00000000	0.000000000	0.02425674	0.009053224	0.00000000	0.002726091	0.0000000	0.000000000	0.000000000	0.000000000	AT4G11400	AT4G11400
PIL5	0.03518538	0.00000000	0.000000000	0.02084504	0.014340344	0.00000000	0.000000000	0.0000000	0.000000000	0.000000000	0.000000000	AT2G20180	PIL5

options(repr.plot.width=6, repr.plot.height=4)
ggplot(cor_rank[1:10,], aes(x=reorder(GeneName, cor, decreasing = FALSE),  y=cor)) + geom_point(size=4)+ 
  labs(title="Cortex-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(cor_rank,"Cortex_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- cor_rank %>% rownames(.)
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
#p4 <- plot_bc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
#p8 <- plot_oc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
#p12 <- plot_ic(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
#p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
#p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
#p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=9)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Cortex ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,nrow=3),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,nrow=3),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,nrow=3),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

## Top 10 only
tf_rank <- cor_rank %>% rownames(.)
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
#p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
#p4 <- plot_bc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
#p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
#p8 <- plot_oc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
#p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
#p12 <- plot_ic(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
#p3 <- grid.grabExpr(draw(p3))
#p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
#p7 <- grid.grabExpr(draw(p7))
#p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
#p11 <- grid.grabExpr(draw(p11))
#p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=5)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Cortex ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,nrow=2),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,nrow=2),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,nrow=2),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Endodermis

end_rank <- bc_rank[which(bc_rank$end*2 > bc_rank$all),]%>% arrange(desc(end))
end_rank$GeneName <- rownames(end_rank)

end_rank

A data.frame: 42 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
MYB36	20.574179871	0.000000000	0.000000000	6.290936886	10.748544272	3.534698713	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G57620	MYB36
MYB74	12.716442815	0.000000000	0.000000000	4.532213333	7.685381936	0.236172960	0.012475395	0.182513773	0.000000000	0.014864734	0.052820684	AT4G05100	MYB74
MYB68	9.699006069	0.000000000	0.000000000	2.501400511	6.179638016	1.017967543	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G65790	MYB68
SOM	7.741216041	0.050269055	0.000000000	2.027707378	4.021560442	1.244198607	0.345040865	0.000000000	0.052439694	0.000000000	0.000000000	AT1G03790	SOM
BLJ	4.557129505	0.000000000	0.000000000	0.643910731	3.719412679	0.193806095	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G14580	BLJ
RAX2	5.318435677	0.147471024	0.049460104	1.185754593	3.465686223	0.450624550	0.019439183	0.000000000	0.000000000	0.000000000	0.000000000	AT2G36890	RAX2
SCR	4.753144752	0.000000000	0.000000000	1.662358141	3.003742831	0.087043779	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT3G54220	SCR
TLP11	4.155878032	0.006012685	0.370838723	0.278142330	2.179886146	0.627860320	0.505965671	0.000000000	0.187172158	0.000000000	0.000000000	AT5G18680	TLP11
SCL3	3.322564901	0.000000000	0.000000000	0.687705471	1.761112367	0.234680845	0.112334395	0.000000000	0.000000000	0.128682075	0.398049747	AT1G50420	SCL3
chr31	1.702067100	0.000000000	0.000000000	0.000000000	1.572727090	0.092754632	0.000000000	0.012541359	0.024044019	0.000000000	0.000000000	AT1G05490	chr31
BIB	1.406028193	0.000000000	0.000000000	0.010856804	1.395171389	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT3G45260	BIB
bZIP58	1.661611692	0.000000000	0.000000000	0.227490055	1.306590616	0.018751076	0.108779944	0.000000000	0.000000000	0.000000000	0.000000000	AT1G13600	bZIP58
MYB122	1.962060042	0.000000000	0.000000000	0.666940898	1.208371730	0.063664777	0.023082637	0.000000000	0.000000000	0.000000000	0.000000000	AT1G74080	MYB122
AGL42	1.244914177	0.000000000	0.000000000	0.446109295	0.726597205	0.042963128	0.029244549	0.000000000	0.000000000	0.000000000	0.000000000	AT5G62165	AGL42
ERF15	1.314549626	0.000000000	0.000000000	0.590902307	0.704993808	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.018653511	AT2G31230	ERF15
AGL102	0.685543758	0.000000000	0.000000000	0.024906311	0.644352188	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.016285259	AT1G47760	AGL102
AT4G38340	0.442251375	0.000000000	0.000000000	0.014497423	0.427753952	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G38340	AT4G38340
SIGA	0.430563439	0.000000000	0.000000000	0.030488331	0.301306106	0.059055838	0.035934757	0.000000000	0.000000000	0.000000000	0.003778406	AT1G64860	SIGA
AT2G43140	0.335394776	0.000000000	0.000000000	0.066194151	0.269200625	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G43140	AT2G43140
TIFY8	0.439795518	0.000000000	0.000000000	0.011583012	0.253202346	0.045342907	0.000000000	0.000000000	0.034522976	0.056150682	0.038993595	AT4G32570	TIFY8
AT5G26749	0.384931821	0.076016044	0.030910265	0.003880024	0.230091801	0.028597865	0.015435821	0.000000000	0.000000000	0.000000000	0.000000000	AT5G26749	AT5G26749
FAR1	0.361206182	0.000000000	0.003293898	0.036799976	0.189857545	0.025975440	0.057755511	0.000000000	0.040380886	0.007142926	0.000000000	AT4G15090	FAR1
ULT2	0.355119532	0.176242108	0.000000000	0.000000000	0.178877425	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G20825	ULT2
AT2G33720	0.157034612	0.000000000	0.000000000	0.004145189	0.152889423	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G33720	AT2G33720
AMS	0.129131872	0.000000000	0.000000000	0.006228522	0.122903350	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G16910	AMS
NF-YA2	0.188031899	0.000000000	0.000000000	0.006204071	0.107483285	0.004237288	0.070107255	0.000000000	0.000000000	0.000000000	0.000000000	AT3G05690	NF-YA2
ERF73	0.142871090	0.000000000	0.000000000	0.000000000	0.096760814	0.007544386	0.005302323	0.000000000	0.000000000	0.022000456	0.011263112	AT1G72360	ERF73
AT1G18335	0.184235887	0.011770842	0.030612477	0.000000000	0.094516912	0.014560717	0.007418725	0.006287192	0.019069022	0.000000000	0.000000000	AT1G18335	AT1G18335
AT4G00390	0.072093134	0.000000000	0.000000000	0.000000000	0.072093134	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT4G00390	AT4G00390
AT3G18870	0.079208553	0.000000000	0.000000000	0.010395189	0.062316358	0.000000000	0.000000000	0.000000000	0.006497006	0.000000000	0.000000000	AT3G18870	AT3G18870
AT1G76880	0.066020974	0.000000000	0.000000000	0.000000000	0.061949340	0.000000000	0.000000000	0.000000000	0.004071634	0.000000000	0.000000000	AT1G76880	AT1G76880
HB28	0.089080723	0.011770842	0.009373312	0.012041042	0.055895527	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT3G50890	HB28
NF-YA6	0.051314786	0.000000000	0.000000000	0.000000000	0.041779659	0.000000000	0.009535127	0.000000000	0.000000000	0.000000000	0.000000000	AT3G14020	NF-YA6
4-Sep	0.061140978	0.000000000	0.020499200	0.000000000	0.040641778	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT2G03710	4-Sep
FD	0.052058744	0.000000000	0.000000000	0.010984046	0.031031246	0.000000000	0.000000000	0.000000000	0.010043451	0.000000000	0.000000000	AT4G35900	FD
AT3G07500	0.054236833	0.000000000	0.000000000	0.000000000	0.028488413	0.025748421	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT3G07500	AT3G07500
AGL67	0.023112209	0.000000000	0.000000000	0.000000000	0.023112209	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT1G77950	AGL67
AT1G61970	0.039740148	0.000000000	0.000000000	0.000000000	0.022206610	0.000000000	0.006785515	0.000000000	0.000000000	0.000000000	0.010748023	AT1G61970	AT1G61970
DUO1	0.014699561	0.000000000	0.000000000	0.000000000	0.009187955	0.000000000	0.005511606	0.000000000	0.000000000	0.000000000	0.000000000	AT3G60460	DUO1
AGL13	0.010731926	0.000000000	0.000000000	0.000000000	0.008604207	0.000000000	0.002127720	0.000000000	0.000000000	0.000000000	0.000000000	AT3G61120	AGL13
AGL62	0.007648184	0.000000000	0.000000000	0.000000000	0.007648184	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G60440	AGL62
TDF1	0.006692161	0.000000000	0.000000000	0.000000000	0.006692161	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT3G28470	TDF1

options(repr.plot.width=8, repr.plot.height=4)
ggplot(end_rank[1:10,], aes(x=reorder(GeneName, end, decreasing = FALSE),  y=end)) + geom_point(size=4)+ 
  labs(title="Endodermis-specific TF Prioritization",x="", y = "Combined centrality score (betweeness, out and in degree)")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(end_rank,"Endodermis_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- end_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Endodermis ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

tf_rank <- end_rank %>% rownames(.)
# Max 10
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
#p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
#p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
#p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
#p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
#p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
#p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
#p3 <- grid.grabExpr(draw(p3))
#p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
#p7 <- grid.grabExpr(draw(p7))
#p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
#p11 <- grid.grabExpr(draw(p11))
#p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=5)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Endodermis ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,nrow=2),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,nrow=2),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,nrow=2),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5]) + plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10]) 
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5]) + plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10]) 
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5]) + plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10]) 

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=16, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Pericycle

per_rank <- bc_rank[which(bc_rank$per*2 > bc_rank$all),]%>% arrange(desc(per))
per_rank$GeneName <- rownames(per_rank)

per_rank

A data.frame: 19 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
MYBC1	14.91041315	0.00000000	0.00000000	0.65860261	2.435376723	8.72219752	2.90627762	0.000000000	0.187958672	0.00000000	0	AT2G40970	MYBC1
AT4G29100	15.42626741	0.00000000	0.00000000	0.04247104	0.180997736	7.75003971	5.13998592	0.000000000	2.312773002	0.00000000	0	AT4G29100	AT4G29100
NUC	6.13113863	0.00000000	0.00000000	0.41835765	0.098575284	4.34553862	1.04180510	0.000000000	0.226861976	0.00000000	0	AT5G44160	NUC
MGP	4.89894588	0.25223575	0.00000000	0.20479261	0.109763013	3.69040420	0.63394150	0.000000000	0.007808814	0.00000000	0	AT1G03840	MGP
AT3G21330	4.37929863	0.16406137	0.00000000	0.09196824	0.153719057	3.39359729	0.57595268	0.000000000	0.000000000	0.00000000	0	AT3G21330	AT3G21330
LBD14	4.27221824	0.00000000	0.00000000	0.01623535	0.051884948	3.37129302	0.83280492	0.000000000	0.000000000	0.00000000	0	AT2G31310	LBD14
IDD11	4.79842980	0.00000000	0.00000000	0.00000000	0.000000000	3.20404576	1.59438404	0.000000000	0.000000000	0.00000000	0	AT3G13810	IDD11
AT2G20100	4.29588276	0.00000000	0.00000000	0.00000000	0.032630438	2.82049079	1.26913958	0.173621948	0.000000000	0.00000000	0	AT2G20100	AT2G20100
GATA23	3.85660749	0.00000000	0.00000000	0.00000000	0.007874926	2.67696633	1.17176623	0.000000000	0.000000000	0.00000000	0	AT5G26930	GATA23
AT2G14880	4.16782313	0.43577425	0.04114576	0.35557855	0.287664612	2.26879846	0.77886149	0.000000000	0.000000000	0.00000000	0	AT2G14880	AT2G14880
SAP	2.74523806	0.12877896	0.00000000	0.25676267	0.093279496	2.14462678	0.12179016	0.000000000	0.000000000	0.00000000	0	AT5G35770	SAP
LAS	0.51091872	0.00000000	0.00000000	0.00000000	0.053461698	0.33109447	0.12636255	0.000000000	0.000000000	0.00000000	0	AT1G55580	LAS
DEL1	0.43576435	0.04833297	0.03480252	0.02158295	0.019448661	0.23401830	0.00000000	0.002508272	0.037835401	0.03723528	0	AT3G48160	DEL1
BOP2	0.34399626	0.00000000	0.00000000	0.00000000	0.003074926	0.20966660	0.13125473	0.000000000	0.000000000	0.00000000	0	AT2G41370	BOP2
AT1G78930	0.12300974	0.00000000	0.00000000	0.00000000	0.002868069	0.08547529	0.03466638	0.000000000	0.000000000	0.00000000	0	AT1G78930	AT1G78930
AT4G31060	0.14188633	0.00000000	0.00000000	0.00000000	0.000000000	0.07268985	0.01588433	0.000000000	0.025452986	0.02785916	0	AT4G31060	AT4G31060
WOX14	0.10054729	0.00000000	0.00000000	0.00000000	0.000000000	0.05461600	0.04342302	0.002508272	0.000000000	0.00000000	0	AT1G20700	WOX14
AT3G61550	0.04333459	0.00000000	0.00000000	0.00000000	0.000000000	0.04333459	0.00000000	0.000000000	0.000000000	0.00000000	0	AT3G61550	AT3G61550
PRR3	0.02667455	0.00000000	0.00000000	0.00000000	0.000000000	0.02667455	0.00000000	0.000000000	0.000000000	0.00000000	0	AT5G60100	PRR3

options(repr.plot.width=6, repr.plot.height=4)
ggplot(per_rank[1:10,], aes(x=reorder(GeneName, per, decreasing = FALSE),  y=per)) + geom_point(size=4)+ 
  labs(title="Pericycle-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(per_rank,"Pericycle_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- per_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Pericycle ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Procambium

pro_rank <- bc_rank[which(bc_rank$pro*2 > bc_rank$all),]%>% arrange(desc(pro))
pro_rank$GeneName <- rownames(pro_rank)

pro_rank

A data.frame: 30 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
MYB43	16.063077043	0.00000000	0.000000000	0.00000000	0.018462790	4.879714006	8.047714481	0.240494235	2.876691531	0.000000000	0.000000000	AT5G16600	MYB43
ABO3	12.418181467	0.38711177	0.072070692	0.00000000	0.067673475	4.010305263	7.290287611	0.174468836	0.416263819	0.000000000	0.000000000	AT1G66600	ABO3
TCP15	9.177076070	0.00000000	0.000000000	0.00000000	0.000000000	0.649598318	4.681848534	2.137878696	1.707750521	0.000000000	0.000000000	AT1G69690	TCP15
HAT9	5.492151241	0.06870023	0.000000000	0.01447595	0.013073992	1.585263738	3.699553013	0.000000000	0.111084324	0.000000000	0.000000000	AT2G22800	HAT9
NAC001	5.586097156	0.00000000	0.004202144	0.48247334	0.179606108	1.415983701	2.859578995	0.006254167	0.637998702	0.000000000	0.000000000	AT1G01010	NAC001
ERF12	4.871129058	0.00000000	0.000000000	0.00000000	0.012143626	1.683704940	2.816567231	0.000000000	0.358713261	0.000000000	0.000000000	AT1G28360	ERF12
IDD14	5.343304900	0.00000000	0.000000000	0.01079147	0.003872669	1.393400783	2.734130023	0.391099760	0.810010191	0.000000000	0.000000000	AT1G68130	IDD14
AT1G75490	2.958317094	0.00000000	0.000000000	0.00000000	0.000000000	0.170210951	2.465071484	0.000000000	0.310691582	0.000000000	0.012343077	AT1G75490	AT1G75490
AT4G20970	2.161550767	0.00000000	0.000000000	0.00000000	0.002868069	0.427155755	1.678458718	0.000000000	0.053068226	0.000000000	0.000000000	AT4G20970	AT4G20970
AT2G40200	2.380382330	0.00000000	0.012278472	0.00000000	0.000000000	0.371988922	1.675323042	0.000000000	0.320791894	0.000000000	0.000000000	AT2G40200	AT2G40200
SHY2	2.164073331	0.00000000	0.000000000	0.00000000	0.000000000	0.250035517	1.427622721	0.181634565	0.304780528	0.000000000	0.000000000	AT1G04240	SHY2
AT4G25410	2.132630161	0.05040275	0.049127548	0.00000000	0.032204709	0.315071899	1.322093151	0.055658640	0.308071466	0.000000000	0.000000000	AT4G25410	AT4G25410
NAC080	1.824714942	0.00000000	0.000000000	0.00000000	0.010723110	0.289794777	1.304852705	0.000000000	0.189405441	0.024611422	0.005327487	AT5G07680	NAC080
NAC045	1.652836169	0.00000000	0.000000000	0.00000000	0.000000000	0.136672103	0.867559280	0.040550450	0.608054336	0.000000000	0.000000000	AT3G03200	NAC045
HB18	1.179598985	0.00000000	0.000000000	0.00000000	0.006692161	0.258665767	0.753756147	0.029984702	0.130500209	0.000000000	0.000000000	AT1G70920	HB18
ARF19	1.032683225	0.01713678	0.064638504	0.03561580	0.036868764	0.167644718	0.575714484	0.013745958	0.067300926	0.000000000	0.054017294	AT1G19220	ARF19
LBD29	0.594929037	0.00000000	0.000000000	0.00000000	0.000000000	0.057825252	0.537103785	0.000000000	0.000000000	0.000000000	0.000000000	AT3G58190	LBD29
STH	0.716741910	0.00000000	0.000000000	0.02066151	0.008604207	0.102889289	0.358427175	0.021816819	0.050331909	0.066920121	0.087090879	AT2G31380	STH
AT1G24210	0.197216762	0.00000000	0.000000000	0.00000000	0.006692161	0.065776052	0.124748549	0.000000000	0.000000000	0.000000000	0.000000000	AT1G24210	AT1G24210
AT4G27240	0.162221139	0.00000000	0.000000000	0.02158295	0.009053224	0.030583695	0.095433302	0.000000000	0.005567970	0.000000000	0.000000000	AT4G27240	AT4G27240
DAR6	0.103486477	0.00776833	0.000000000	0.00000000	0.003824092	0.000000000	0.091894056	0.000000000	0.000000000	0.000000000	0.000000000	AT5G66620	DAR6
WRKY67	0.158987824	0.00000000	0.000000000	0.00000000	0.002868069	0.075702136	0.080417619	0.000000000	0.000000000	0.000000000	0.000000000	AT1G66550	WRKY67
AT5G55580	0.123376860	0.00000000	0.000000000	0.00000000	0.000000000	0.041387259	0.074846675	0.000000000	0.000000000	0.007142926	0.000000000	AT5G55580	AT5G55580
AT1G74120	0.057441290	0.00000000	0.000000000	0.00000000	0.009560229	0.000000000	0.033422822	0.000000000	0.014458239	0.000000000	0.000000000	AT1G74120	AT1G74120
EMB3114	0.022625319	0.00000000	0.000000000	0.00000000	0.003824092	0.006091497	0.012709730	0.000000000	0.000000000	0.000000000	0.000000000	AT2G36000	EMB3114
NF-YA8	0.018139334	0.00000000	0.000000000	0.00000000	0.008604207	0.000000000	0.009535127	0.000000000	0.000000000	0.000000000	0.000000000	AT1G17590	NF-YA8
HB51	0.008649420	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.008649420	0.000000000	0.000000000	0.000000000	0.000000000	AT5G03790	HB51
IAA32	0.010764473	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.007418725	0.000000000	0.003345748	0.000000000	0.000000000	AT2G01200	IAA32
MYB95	0.005860963	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.005860963	0.000000000	0.000000000	0.000000000	0.000000000	AT1G74430	MYB95
AT1G18960	0.003733243	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.003733243	0.000000000	0.000000000	0.000000000	0.000000000	AT1G18960	AT1G18960

options(repr.plot.width=6, repr.plot.height=4)
ggplot(pro_rank[1:10,], aes(x=reorder(GeneName, pro, decreasing = FALSE),  y=pro)) + geom_point(size=4)+ 
  labs(title="Procambium-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(pro_rank,"Procambium_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- pro_rank %>% rownames(.)
# Max 30
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_blank(tf_rank[15])

p4 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p5 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p6 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_blank(tf_rank[15])

p7 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p8 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p9 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_blank(tf_rank[15])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=10)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Procambium ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,nrow=3),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p4,p5,p6,nrow=3),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p7,p8,p9,nrow=3),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Phloem

phl_rank <- bc_rank[which(bc_rank$phl*2 > bc_rank$all),]%>% arrange(desc(phl))
phl_rank$GeneName <- rownames(phl_rank)

phl_rank

A data.frame: 50 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
APL	17.844322073	0.00000000	0.000000000	0.000000000	0.000000000	1.895694774	1.714937260	2.164953147	12.068736892	0.000000000	0.000000000	AT1G79430	APL
OBP2	19.626516178	0.00000000	0.000000000	0.000000000	0.000000000	2.886506004	5.836194997	0.015082655	10.888732521	0.000000000	0.000000000	AT1G07640	OBP2
AT3G12730	9.807026857	0.00000000	0.000000000	0.000000000	0.000000000	1.085095947	0.202927880	0.000000000	8.519003030	0.000000000	0.000000000	AT3G12730	AT3G12730
DOF6	10.369908100	0.00000000	0.000000000	0.000000000	0.000000000	1.293766765	0.462508272	1.782009903	6.831623160	0.000000000	0.000000000	AT3G45610	DOF6
AT2G03500	8.337749613	0.00000000	0.000000000	0.000000000	0.000000000	1.110191244	1.136420947	0.020165247	6.070972175	0.000000000	0.000000000	AT2G03500	AT2G03500
DAR2	8.618352004	0.00000000	0.000000000	0.000000000	0.000000000	1.143452224	1.108834134	0.468422625	5.897643021	0.000000000	0.000000000	AT2G39830	DAR2
AT2G28810	9.426614047	0.00000000	0.000000000	0.000000000	0.000000000	1.580162327	2.663112048	0.026254293	5.157085378	0.000000000	0.000000000	AT2G28810	AT2G28810
HCA2	5.517875746	0.00000000	0.000000000	0.000000000	0.000000000	0.348325274	0.231431791	0.015214752	4.922903929	0.000000000	0.000000000	AT5G62940	HCA2
NAC057	5.520438511	0.00000000	0.000000000	0.000000000	0.000000000	0.345280579	0.512603280	0.015214752	4.647339899	0.000000000	0.000000000	AT3G17730	NAC057
AT5G41380	4.485095958	0.00000000	0.000000000	0.000000000	0.000000000	0.105076026	0.019805580	0.000000000	4.360214352	0.000000000	0.000000000	AT5G41380	AT5G41380
AT4G37180	5.223395955	0.00793207	0.007472171	0.005836971	0.048259729	0.649975317	0.323930557	0.017623951	3.967073511	0.084287560	0.111004117	AT4G37180	AT4G37180
NAC020	4.474851299	0.00000000	0.000000000	0.000000000	0.000000000	0.686697179	0.328105413	0.013878056	3.446170651	0.000000000	0.000000000	AT1G54330	NAC020
NAC2	3.496258409	0.00000000	0.000000000	0.000000000	0.000000000	0.308778632	0.425776168	0.000000000	2.761703609	0.000000000	0.000000000	AT3G15510	NAC2
bZIP19	4.262023240	0.14100327	0.014058465	0.030897113	0.076054930	0.450617496	0.823885719	0.016320279	2.709185973	0.000000000	0.000000000	AT4G35040	bZIP19
HB21	4.459722485	0.00000000	0.000000000	0.000000000	0.025413486	0.482174192	1.358219706	0.000000000	2.593915101	0.000000000	0.000000000	AT2G18550	HB21
AT1G49560	4.767954053	0.32088670	0.008864929	0.424452385	0.399695412	0.230026185	0.135960540	0.030264383	2.448177777	0.294146744	0.475478996	AT1G49560	AT1G49560
REM22	2.822338450	0.00000000	0.000000000	0.000000000	0.000000000	0.198746448	0.225280767	0.006221143	2.392090092	0.000000000	0.000000000	AT3G17010	REM22
FLP	4.235152604	0.56810859	0.031657435	0.107744382	0.117251965	0.175675640	0.225020013	0.016353303	2.270932524	0.506727685	0.215681071	AT1G14350	FLP
AT1G63820	3.720006790	0.00000000	0.000000000	0.000000000	0.009329967	0.848743347	0.782290821	0.000000000	2.079642654	0.000000000	0.000000000	AT1G63820	AT1G63820
AT1G26790	2.910254699	0.00000000	0.000000000	0.000000000	0.000000000	0.179393715	0.660556980	0.000000000	2.070304004	0.000000000	0.000000000	AT1G26790	AT1G26790
CRF1	3.720408554	0.21276123	0.206976144	0.261873284	0.324733096	0.308264664	0.138417643	0.000000000	1.993441722	0.195940122	0.078000645	AT4G11140	CRF1
AT5G02460	3.055381286	0.00000000	0.000000000	0.000000000	0.000000000	0.565451317	0.519269935	0.013812007	1.956848028	0.000000000	0.000000000	AT5G02460	AT5G02460
AT5G63700	3.220104522	0.00000000	0.000000000	0.556733035	0.000000000	0.044349903	0.029599282	0.000000000	1.795400156	0.000000000	0.794022145	AT5G63700	AT5G63700
NAC086	1.671278410	0.00000000	0.000000000	0.000000000	0.000000000	0.058164212	0.000000000	0.000000000	1.613114198	0.000000000	0.000000000	AT5G17260	NAC086
WRKY32	2.765045412	0.00000000	0.000000000	0.000000000	0.070488176	0.566141217	0.478017862	0.007524815	1.558337999	0.059820797	0.024714547	AT4G30935	WRKY32
VOZ1	2.699988128	0.00000000	0.000000000	0.000000000	0.044628126	0.473464461	0.621765050	0.083750499	1.404101573	0.033869726	0.038408694	AT1G28520	VOZ1
MYB10	2.103618150	0.00000000	0.000000000	0.000000000	0.000000000	0.035372330	0.498233312	0.247741073	1.322271434	0.000000000	0.000000000	AT3G12820	MYB10
GATA20	1.684610713	0.00000000	0.000000000	0.000000000	0.000000000	0.277759093	0.092264430	0.000000000	1.314587190	0.000000000	0.000000000	AT2G18380	GATA20
SVP	1.432290958	0.00000000	0.000000000	0.029604337	0.034589965	0.094637812	0.030758331	0.000000000	1.213039110	0.000000000	0.029661402	AT2G22540	SVP
AGL15	1.098345251	0.00000000	0.000000000	0.000000000	0.006692161	0.050236157	0.043390946	0.010132160	0.987893828	0.000000000	0.000000000	AT5G13790	AGL15
KNAT2	1.461220827	0.00000000	0.000000000	0.018909150	0.168804306	0.226054236	0.066045262	0.000000000	0.981407873	0.000000000	0.000000000	AT1G70510	KNAT2
ET2	0.852503850	0.00000000	0.000000000	0.000000000	0.000000000	0.068770232	0.078536301	0.000000000	0.705197317	0.000000000	0.000000000	AT5G56780	ET2
AT1G64530	1.120045796	0.03882994	0.069895732	0.108418064	0.095720622	0.050855970	0.036356583	0.006320216	0.635192817	0.014544308	0.063911545	AT1G64530	AT1G64530
WOX2	0.275019516	0.00000000	0.000000000	0.000000000	0.032162538	0.000000000	0.000000000	0.000000000	0.242856978	0.000000000	0.000000000	AT5G59340	WOX2
AT1G02030	0.314641406	0.00000000	0.004686656	0.000000000	0.031876961	0.000000000	0.000000000	0.000000000	0.225122356	0.052955432	0.000000000	AT1G02030	AT1G02030
AT1G58220	0.339257487	0.00000000	0.006587797	0.000000000	0.028488413	0.000000000	0.028425231	0.017623951	0.200343122	0.012091053	0.045697921	AT1G58220	AT1G58220
AGL80	0.265282542	0.00000000	0.000000000	0.000000000	0.002868069	0.004315187	0.059445379	0.000000000	0.198653907	0.000000000	0.000000000	AT5G48670	AGL80
AT3G06220	0.180725116	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.180725116	0.000000000	0.000000000	AT3G06220	AT3G06220
PIF7	0.270023952	0.00000000	0.023133864	0.092271047	0.001912046	0.000000000	0.000000000	0.000000000	0.152706995	0.000000000	0.000000000	AT5G61270	PIF7
SPL10	0.193594293	0.00000000	0.004202144	0.000000000	0.004780115	0.000000000	0.024395674	0.000000000	0.145557421	0.006118379	0.008540560	AT1G27370	SPL10
SPL13A	0.181543092	0.00000000	0.005195039	0.000000000	0.000000000	0.000000000	0.005454413	0.000000000	0.127166742	0.032245373	0.011481524	AT5G50570	SPL13A
AT5G63080	0.198822428	0.00000000	0.003293898	0.000000000	0.008604207	0.012215950	0.015995872	0.000000000	0.101449741	0.039772184	0.017490575	AT5G63080	AT5G63080
AT3G46070	0.106851330	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.071187905	0.035663425	0.000000000	AT3G46070	AT3G46070
DRD1	0.112080387	0.00000000	0.000000000	0.000000000	0.003824092	0.021170077	0.010144816	0.000000000	0.061376222	0.000000000	0.015565179	AT2G16390	DRD1
AT4G03250	0.098184037	0.00000000	0.014365588	0.000000000	0.004780115	0.000000000	0.012475395	0.000000000	0.060268203	0.000000000	0.006294737	AT4G03250	AT4G03250
TCP24	0.059879916	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.059879916	0.000000000	0.000000000	AT1G30210	TCP24
AT1G68030	0.036969277	0.00000000	0.000000000	0.000000000	0.002868069	0.000000000	0.004059424	0.000000000	0.030041784	0.000000000	0.000000000	AT1G68030	AT1G68030
WRKY50	0.027779512	0.00000000	0.000000000	0.000000000	0.002868069	0.000000000	0.003185921	0.000000000	0.017947116	0.000000000	0.003778406	AT5G26170	WRKY50
AT3G52270	0.014641262	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.014641262	0.000000000	0.000000000	AT3G52270	AT3G52270
OFP11	0.007417382	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.007417382	0.000000000	0.000000000	AT4G14860	OFP11

options(repr.plot.width=6, repr.plot.height=4)
ggplot(phl_rank[1:10,], aes(x=reorder(GeneName, phl, decreasing = FALSE),  y=phl)) + geom_point(size=4)+ 
  labs(title="Phloem-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(phl_rank,"Phloem_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- phl_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Phloem ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Xylem

xyl_rank <- bc_rank[which(bc_rank$xyl*2 > bc_rank$all),]%>% arrange(desc(xyl))
xyl_rank$GeneName <- rownames(xyl_rank)

xyl_rank

A data.frame: 91 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
AT1G68810	11.536622	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.32495975	11.041633	0.17002908	0.000000000	0.00000000	AT1G68810	AT1G68810
VND2	9.638343	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.02646882	9.611874	0.00000000	0.000000000	0.00000000	AT4G36160	VND2
MYB46	8.777686	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	8.777686	0.00000000	0.000000000	0.00000000	AT5G12870	MYB46
ATHB-15	13.243226	0.00000000	0.000000000	0.000000000	0.000000000	0.59770967	3.71766157	8.558721	0.36913403	0.000000000	0.00000000	AT1G52150	ATHB-15
DOF2	10.249995	0.00000000	0.000000000	0.351580305	1.282458163	0.00000000	0.00000000	8.527854	0.00000000	0.050294993	0.03780762	AT3G21270	DOF2
MYB83	8.033038	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	8.033038	0.00000000	0.000000000	0.00000000	AT3G08500	MYB83
VND3	7.788913	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.03811280	7.750800	0.00000000	0.000000000	0.00000000	AT5G66300	VND3
AT1G66810	7.561682	0.01586414	0.026086404	0.000000000	0.000000000	0.00000000	0.00000000	7.400563	0.00000000	0.119168117	0.00000000	AT1G66810	AT1G66810
VND7	7.301973	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	7.301973	0.00000000	0.000000000	0.00000000	AT1G71930	VND7
IAA6	7.188061	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.01379361	7.174267	0.00000000	0.000000000	0.00000000	AT1G52830	IAA6
ZHD3	7.143422	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	7.143422	0.00000000	0.000000000	0.00000000	AT2G02540	ZHD3
IAA31	10.900804	0.00000000	2.802604101	0.000000000	0.000000000	0.03476655	0.63950508	7.043569	0.38035904	0.000000000	0.00000000	AT3G17600	IAA31
PHB	10.332204	0.00000000	0.000000000	0.000000000	0.000000000	0.34165790	1.93126800	6.595116	1.15678338	0.105396182	0.20198271	AT2G34710	PHB
IAA8	10.422576	0.24034784	0.000000000	0.008117677	0.120650839	0.28653914	1.57914862	6.466031	0.28265168	0.888256807	0.55083188	AT2G22670	IAA8
VND4	6.347801	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	6.347801	0.00000000	0.000000000	0.00000000	AT1G12260	VND4
BHLH32	10.652133	0.14112463	0.000000000	0.142577807	0.160337883	3.14212784	1.16852421	5.897440	0.00000000	0.000000000	0.00000000	AT3G25710	BHLH32
VND5	5.622524	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	5.622524	0.00000000	0.000000000	0.00000000	AT1G62700	VND5
XND1	5.577439	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.06468006	5.512758	0.00000000	0.000000000	0.00000000	AT5G64530	XND1
VND1	5.298823	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	5.298823	0.00000000	0.000000000	0.00000000	AT2G18060	VND1
AT1G68200	5.259217	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	5.259217	0.00000000	0.000000000	0.00000000	AT1G68200	AT1G68200
HB31	5.157856	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	5.157856	0.00000000	0.000000000	0.00000000	AT1G14440	HB31
LBD31	4.894236	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	4.894236	0.00000000	0.000000000	0.00000000	AT4G00210	LBD31
NAC075	9.054390	0.00000000	0.000000000	0.000000000	0.000000000	0.35307753	0.21412043	4.541242	3.90193131	0.005966671	0.03805185	AT4G29230	NAC075
HAT14	5.037822	0.00000000	0.000000000	0.000000000	0.000000000	0.08900348	0.54526568	4.403553	0.00000000	0.000000000	0.00000000	AT5G06710	HAT14
LBD18	4.292386	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	4.226754	0.00000000	0.013799569	0.05183261	AT2G45420	LBD18
ASL9	5.441949	0.00000000	0.000000000	0.000000000	0.000000000	0.08214788	0.60827844	3.854973	0.89655011	0.000000000	0.00000000	AT1G16530	ASL9
NAC050	4.775704	0.04478520	0.003293898	0.000000000	0.003824092	0.00000000	0.00000000	3.778609	0.17050597	0.273912293	0.50077366	AT3G10480	NAC050
AT1G27660	6.418503	0.00000000	0.000000000	0.016139067	0.022789488	1.53079207	0.88450150	3.726692	0.23758866	0.000000000	0.00000000	AT1G27660	AT1G27660
JLO	3.741622	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	3.697310	0.00000000	0.013799569	0.03051252	AT4G00220	JLO
AP3	3.754440	0.00000000	0.000000000	0.000000000	0.022734174	0.00000000	0.02203160	3.618079	0.09159528	0.000000000	0.00000000	AT3G54340	AP3
...	...	...	...	...	...	...	...	...	...	...	...	...	...
AT1G24610	1.28532295	0.00000000	0.027324573	0.000000000	0.018993758	0.052527858	0.007418725	1.09533515	0.048403747	0.014273050	0.02104609	AT1G24610	AT1G24610
AT1G26610	1.01572276	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	1.01572276	0.000000000	0.000000000	0.00000000	AT1G26610	AT1G26610
BZIP49	0.98878988	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.98878988	0.000000000	0.000000000	0.00000000	AT3G56660	BZIP49
AT1G21780	1.87033822	0.05770617	0.039336317	0.000000000	0.033268527	0.002863808	0.053975350	0.94878270	0.061103284	0.252282991	0.42101907	AT1G21780	AT1G21780
IWS1	1.72910671	0.06241160	0.063762795	0.034452658	0.135643262	0.101251515	0.142644279	0.93914856	0.129202290	0.000000000	0.12058975	AT1G32130	IWS1
AT5G03500	1.59794087	0.12729866	0.210505817	0.008290378	0.139656952	0.027803453	0.075780962	0.91150633	0.059498573	0.000000000	0.03759974	AT5G03500	AT5G03500
AT4G19630	1.51504406	0.20687737	0.127747630	0.026834254	0.050262016	0.211076971	0.000000000	0.89224582	0.000000000	0.000000000	0.00000000	AT4G19630	AT4G19630
AT3G60580	1.38960450	0.00000000	0.000000000	0.000000000	0.000000000	0.367576083	0.118863217	0.88867730	0.014487896	0.000000000	0.00000000	AT3G60580	AT3G60580
AT3G10470	1.21130906	0.00000000	0.358628010	0.000000000	0.000000000	0.000000000	0.000000000	0.85268105	0.000000000	0.000000000	0.00000000	AT3G10470	AT3G10470
AL2	1.44523637	0.00000000	0.073288053	0.033779102	0.043741070	0.048654959	0.055607945	0.84003587	0.133358571	0.017126642	0.19964416	AT3G11200	AL2
HB34	0.96786493	0.01227990	0.017801472	0.000000000	0.008604207	0.000000000	0.044269749	0.73467419	0.146148303	0.004087115	0.00000000	AT3G28920	HB34
BZIP24	0.69473539	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.036501552	0.63058514	0.027648698	0.000000000	0.00000000	AT3G51960	BZIP24
GIF3	0.68630806	0.00000000	0.000000000	0.004145189	0.030195385	0.000000000	0.041122758	0.61084473	0.000000000	0.000000000	0.00000000	AT4G00850	GIF3
AT5G06770	0.82709697	0.02788150	0.034907787	0.031156037	0.072925874	0.003045749	0.002127720	0.59147018	0.004071634	0.018644751	0.04086574	AT5G06770	AT5G06770
AT3G22100	0.68127060	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.55474879	0.126521815	0.000000000	0.00000000	AT3G22100	AT3G22100
FBH2	0.92274260	0.03968020	0.008404289	0.014127349	0.029444435	0.016600778	0.012369000	0.54712225	0.179537970	0.026085088	0.04937124	AT4G09180	FBH2
AT3G49930	0.71613741	0.01202537	0.049425790	0.047856345	0.097644391	0.010539127	0.000000000	0.45993985	0.028475042	0.000000000	0.01023149	AT3G49930	AT3G49930
JAZ11	0.76266853	0.05672855	0.026775396	0.014127349	0.031067421	0.016929972	0.010724510	0.39486179	0.192179273	0.005966671	0.01330760	AT3G43440	JAZ11
PIF4	0.43532347	0.00000000	0.000000000	0.000000000	0.003824092	0.000000000	0.005511606	0.37184351	0.054144264	0.000000000	0.00000000	AT2G43010	PIF4
AT5G18090	0.52826362	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.31818293	0.105024189	0.069530228	0.03552627	AT5G18090	AT5G18090
ARR15	0.29865152	0.00000000	0.000000000	0.015444015	0.000000000	0.000000000	0.000000000	0.28320751	0.000000000	0.000000000	0.00000000	AT1G74890	ARR15
AGL64	0.27514793	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.27514793	0.000000000	0.000000000	0.00000000	AT1G29962	AGL64
AT3G45880	0.41074758	0.08731405	0.028867289	0.000000000	0.011472275	0.000000000	0.000000000	0.24281748	0.040276483	0.000000000	0.00000000	AT3G45880	AT3G45880
AT1G24040	0.27513044	0.00000000	0.000000000	0.000000000	0.002889449	0.000000000	0.000000000	0.21701485	0.000000000	0.055226142	0.00000000	AT1G24040	AT1G24040
AT1G76870	0.33154241	0.00000000	0.000000000	0.000000000	0.000000000	0.007189877	0.051141693	0.21518991	0.058020931	0.000000000	0.00000000	AT1G76870	AT1G76870
AT2G22200	0.20417819	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.15794307	0.000000000	0.046235127	0.00000000	AT2G22200	AT2G22200
OFP10	0.09384749	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.09384749	0.000000000	0.000000000	0.00000000	AT5G22240	OFP10
AT4G08250	0.10688739	0.00000000	0.012352944	0.000000000	0.024140650	0.000000000	0.000000000	0.07039380	0.000000000	0.000000000	0.00000000	AT4G08250	AT4G08250
ORC1A	0.05185896	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.03223832	0.000000000	0.019620644	0.00000000	AT4G14700	ORC1A
AT5G46915	0.04334105	0.00000000	0.000000000	0.000000000	0.000000000	0.015078047	0.000000000	0.02826301	0.000000000	0.000000000	0.00000000	AT5G46915	AT5G46915

options(repr.plot.width=6, repr.plot.height=4)
ggplot(xyl_rank[1:10,], aes(x=reorder(GeneName, xyl, decreasing = FALSE),  y=xyl)) + geom_point(size=4)+ 
  labs(title="Xylem-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(xyl_rank,"Xylem_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- xyl_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Xylem ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

LRC

lrc_rank <- bc_rank[which(bc_rank$lrc*2 > bc_rank$all),]%>% arrange(desc(lrc))
lrc_rank$GeneName <- rownames(lrc_rank)

lrc_rank

A data.frame: 28 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
AT1G68920	12.028456518	0.80269789	0.066031541	0.35244162	0.129029370	0.000000000	0.000000000	0.00000000	0.050039116	6.069123864	4.55909312	AT1G68920	AT1G68920
ANL2	9.794258630	2.66677616	0.479300264	0.57303214	0.151216222	0.000000000	0.000000000	0.00000000	0.000000000	4.977955879	0.94597797	AT4G00730	ANL2
OFP6	4.865209570	0.81111328	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	4.031024308	0.02307198	AT3G52525	OFP6
ASL1	7.139711374	0.92904994	0.160306017	0.21414076	0.080168941	0.000000000	0.000000000	0.00000000	0.000000000	3.587946721	2.16809900	AT5G66870	ASL1
AT1G05805	6.422509285	0.53216520	0.286505224	0.08255675	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	3.228166038	2.29311608	AT1G05805	AT1G05805
AT1G69030	5.649837921	0.41092076	0.120856558	0.16213030	0.301310724	0.032585593	0.007205912	0.79317340	0.143402744	3.134753625	0.54349831	AT1G69030	AT1G69030
AT5G48560	5.877041418	0.56988054	0.028008727	0.16405786	0.095136546	0.038354731	0.138869733	0.00000000	0.174961065	3.118548124	1.54922409	AT5G48560	AT5G48560
AIL5	5.291341118	0.47478752	0.016601883	0.10745552	0.040084471	0.000000000	0.022200745	0.00000000	0.000000000	3.104543481	1.52566750	AT5G57390	AIL5
WRKY11	5.311201179	0.75893219	0.333131627	0.17338140	0.166824582	0.111499756	0.260780670	0.20035394	0.055042565	2.793604747	0.45764970	AT4G31550	WRKY11
AT3G60670	4.491201102	0.19804070	0.000000000	0.09818241	0.003824092	0.000000000	0.004118849	0.00000000	0.000000000	2.628930196	1.55810485	AT3G60670	AT3G60670
LBD4	4.621108722	0.73410069	0.039018397	0.26332825	0.148908920	0.000000000	0.000000000	0.00000000	0.018969584	2.348401137	1.06838175	AT1G31320	LBD4
AT1G21000	4.413675536	0.18764047	0.053546095	0.00000000	0.008604207	0.208504162	0.434159716	0.00000000	0.359134919	2.319946975	0.84213899	AT1G21000	AT1G21000
AT5G62610	4.098912215	0.52985201	0.169729962	0.15988380	0.157210322	0.023803603	0.000000000	0.03932829	0.123812281	2.152560271	0.74273168	AT5G62610	AT5G62610
AT1G09250	4.002871998	0.44767014	0.063171757	0.31825713	0.257940443	0.069915289	0.041396054	0.00000000	0.100285399	2.075494388	0.62874139	AT1G09250	AT1G09250
GRF2	2.386541167	0.37199083	0.163265493	0.15298413	0.061251027	0.026649615	0.000000000	0.00000000	0.023895361	1.487990391	0.09851432	AT4G37740	GRF2
HSF A4A	2.228440554	0.06769198	0.018463845	0.06155743	0.159372779	0.007012623	0.023786766	0.00000000	0.020296272	1.173419793	0.69683906	AT4G18880	HSF A4A
AGL20	1.676238446	0.05574800	0.000000000	0.03256699	0.023600954	0.000000000	0.000000000	0.00000000	0.000000000	1.146423683	0.41789882	AT2G45660	AGL20
TOE3	2.056734382	0.11261032	0.000000000	0.31506886	0.125307966	0.000000000	0.004059424	0.02406962	0.028609874	1.081520509	0.36548781	AT5G67180	TOE3
NAC016	1.560401930	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.801524240	0.75887769	AT1G34180	NAC016
MYB3R-4	1.184472030	0.12311429	0.000000000	0.12720903	0.031963529	0.044776504	0.016561928	0.00000000	0.007808814	0.659143499	0.17389444	AT5G11510	MYB3R-4
AGL94	1.221091071	0.04501335	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.615888474	0.56018925	AT1G69540	AGL94
HDG2	0.426117788	0.05843617	0.016601883	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.248885013	0.10219472	AT1G05230	HDG2
AT2G20110	0.282612672	0.01611099	0.000000000	0.01079147	0.006692161	0.000000000	0.010963788	0.00000000	0.000000000	0.142870875	0.09518338	AT2G20110	AT2G20110
AT5G25790	0.198890384	0.00000000	0.000000000	0.00000000	0.006692161	0.000000000	0.000000000	0.00000000	0.000000000	0.105738326	0.08645990	AT5G25790	AT5G25790
NAC063	0.121166254	0.02290884	0.003293898	0.00000000	0.000000000	0.000000000	0.000000000	0.00755784	0.000000000	0.087405673	0.00000000	AT3G55210	NAC063
HSFC1	0.105923308	0.00000000	0.000000000	0.00000000	0.000000000	0.008592402	0.018994153	0.00000000	0.007815022	0.053203407	0.01731832	AT3G24520	HSFC1
AT3G19184	0.079582042	0.00000000	0.019069529	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.014153900	0.046358612	0.00000000	AT3G19184	AT3G19184
AT2G23060	0.005360395	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.005360395	0.00000000	AT2G23060	AT2G23060

options(repr.plot.width=6, repr.plot.height=4)
ggplot(lrc_rank[1:10,], aes(x=reorder(GeneName, lrc, decreasing = FALSE),  y=lrc)) + geom_point(size=4)+ 
  labs(title="LRC-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(lrc_rank,"LRC_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- lrc_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Lateral Root Cap ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Columella

col_rank <- bc_rank[which(bc_rank$col*2 > bc_rank$all),]%>% arrange(desc(col))
col_rank$GeneName <- rownames(col_rank)

col_rank

A data.frame: 48 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
NAC042	15.848132176	0.013811893	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	6.854929780	8.979390503	AT2G43000	NAC042
RAP2.4	10.006093142	0.186297480	0.008448429	0.282168255	0.182106509	0.116762648	0.290389873	0.178080743	0.096254210	2.979364487	5.686220508	AT1G78080	RAP2.4
LBD41	9.456000605	0.307008709	0.191520974	0.000000000	0.070337772	0.069542655	0.139797662	0.003778920	0.000000000	3.469790935	5.204222979	AT3G02550	LBD41
AT3G52440	9.429183334	0.985446142	0.000000000	0.172878368	0.012611943	0.000000000	0.000000000	0.000000000	0.000000000	3.236305265	5.021941616	AT3G52440	AT3G52440
WRKY33	8.874355007	0.452522181	0.137972930	0.898694396	0.359952109	0.061746041	0.019995697	0.000000000	0.000000000	2.119999332	4.823472321	AT2G38470	WRKY33
FBH4	8.394265863	0.095062463	0.016515136	0.377258006	0.020283339	0.000000000	0.000000000	0.000000000	0.000000000	3.482554761	4.402592158	AT2G42280	FBH4
NAM	5.032279576	0.049435808	0.000000000	0.087143552	0.012428298	0.011567265	0.035736305	0.000000000	0.000000000	1.742243550	3.093724799	AT1G52880	NAM
MYB55	5.168010880	0.000000000	0.000000000	0.204637710	0.037920439	0.190036368	0.542549136	0.000000000	0.670759127	0.556416987	2.965691113	AT4G01680	MYB55
ARF10	5.331093835	0.311459849	0.005195039	0.063037907	0.091195214	0.000000000	0.000000000	0.052948378	0.059813008	1.805865438	2.941579001	AT2G28350	ARF10
MIF3	5.021811302	0.000000000	0.000000000	0.000000000	0.177225730	0.000000000	0.000000000	0.000000000	0.000000000	1.980454559	2.864131012	AT1G18835	MIF3
WRKY26	4.693762837	0.085164280	0.048726749	0.000000000	0.000000000	0.124044165	0.090023319	0.000000000	0.077082119	1.454293162	2.814429043	AT5G07100	WRKY26
BZIP25	3.834485078	0.354132474	0.090644091	0.217755390	0.126487469	0.014011226	0.017541971	0.000000000	0.041656730	0.635647021	2.336608705	AT3G54620	BZIP25
AT2G41835	2.692651000	0.105482633	0.013599328	0.000000000	0.008604207	0.000000000	0.000000000	0.000000000	0.000000000	0.497079177	2.067885655	AT2G41835	AT2G41835
TGA7	3.906170154	0.095375083	0.182361754	0.769839262	0.009767087	0.000000000	0.006904364	0.000000000	0.000000000	0.875760393	1.966162211	AT1G77920	TGA7
AT3G25790	3.229468530	0.128992325	0.026093609	0.215806365	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.930766326	1.927809906	AT3G25790	AT3G25790
SNI1	2.546469452	0.000000000	0.000000000	0.024256744	0.000000000	0.000000000	0.011021681	0.453197944	0.108123713	0.271128853	1.678740517	AT4G18470	SNI1
NAC052	2.982878435	0.124093179	0.006325287	0.012262866	0.049810258	0.021751524	0.045356986	0.104789021	0.025698804	1.016760611	1.576029899	AT3G10490	NAC052
RR1	2.566895589	0.214778894	0.057848262	0.167176491	0.027992494	0.026105660	0.025236475	0.003778920	0.074660945	0.401287262	1.568030186	AT3G16857	RR1
HMG	2.763025764	0.059576067	0.023371173	0.357420825	0.169862373	0.148964595	0.110386580	0.011336759	0.116902434	0.283892260	1.481312697	AT3G28730	HMG
TGA4	2.482817925	0.055247572	0.072129849	0.209752180	0.091251579	0.068634856	0.202400789	0.000000000	0.234746171	0.266108661	1.282546268	AT5G10030	TGA4
AT2G42300	2.238305659	0.098758750	0.025975195	0.094449464	0.002868069	0.000000000	0.000000000	0.000000000	0.015066107	0.774068687	1.227119386	AT2G42300	AT2G42300
TLP7	2.134130449	0.061285337	0.031008740	0.129016209	0.037198088	0.008244660	0.057619057	0.030871090	0.073595932	0.537622092	1.167669243	AT1G53320	TLP7
BRM	2.268815558	0.117827795	0.026951453	0.246565813	0.079885378	0.026107770	0.052947449	0.007557840	0.070781273	0.487394849	1.152795939	AT2G46020	BRM
SYD	2.204603753	0.094800415	0.007096180	0.040777167	0.049819092	0.083455852	0.113237151	0.021369847	0.199344946	0.459097888	1.135605216	AT2G28290	SYD
CHR11	1.951484413	0.051771620	0.007980554	0.007793918	0.039761725	0.070375125	0.090240263	0.126614290	0.174916038	0.356507473	1.025523407	AT3G06400	CHR11
EIN3	1.538177010	0.077061788	0.032686672	0.088661842	0.046762834	0.004849074	0.021091991	0.012574383	0.030242517	0.286814863	0.937431046	AT3G20770	EIN3
ATRX	1.726883317	0.045821061	0.012549096	0.030161508	0.049839236	0.087337280	0.091132433	0.013845031	0.157725436	0.305210413	0.933261821	AT1G08600	ATRX
AT1G02080	1.791566361	0.080962172	0.024206446	0.103334933	0.029782074	0.031449072	0.061932325	0.110579720	0.183101466	0.266337879	0.899880274	AT1G02080	AT1G02080
PC-MYB1	1.429478998	0.083408050	0.021664531	0.061121765	0.030588131	0.000000000	0.002726091	0.010099136	0.062012385	0.349432679	0.808426232	AT4G32730	PC-MYB1
AT2G44430	1.589365661	0.130997472	0.069199895	0.132295825	0.093511565	0.000000000	0.000000000	0.030981837	0.012891105	0.317526105	0.801961857	AT2G44430	AT2G44430
SDG2	1.390375312	0.059448729	0.053174183	0.122279279	0.053853321	0.008917847	0.024841387	0.007557840	0.054941038	0.224846411	0.780515277	AT4G15180	SDG2
EICBP.B	1.472034674	0.073448730	0.051128746	0.202014821	0.050546226	0.004604770	0.006329047	0.018861575	0.115464069	0.210166015	0.739470676	AT5G09410	EICBP.B
AT5G14140	1.330142648	0.000000000	0.062621708	0.019294295	0.105427844	0.012460976	0.008476926	0.000000000	0.055676774	0.355338767	0.710845358	AT5G14140	AT5G14140
AT5G65910	1.309393705	0.056069380	0.030950543	0.039829220	0.101513043	0.042078039	0.029737762	0.112702700	0.060372148	0.174146154	0.661994717	AT5G65910	AT5G65910
MBD9	1.195668436	0.119929174	0.031589336	0.000000000	0.007648184	0.000000000	0.019765082	0.005049568	0.045507686	0.364661169	0.601518238	AT3G01460	MBD9
E2F1	0.953848255	0.031720267	0.058304625	0.005836971	0.009560229	0.015083754	0.007171406	0.000000000	0.030631302	0.303211449	0.492328251	AT5G22220	E2F1
DRIP2	0.696898860	0.011770842	0.008864929	0.000000000	0.002868069	0.002982303	0.043636687	0.011336759	0.007830786	0.132174319	0.475434165	AT2G30580	DRIP2
AT3G08505	0.744365710	0.000000000	0.043199141	0.000000000	0.002868069	0.008920351	0.043766669	0.000000000	0.000000000	0.232433819	0.413177661	AT3G08505	AT3G08505
AT3G54460	0.707080513	0.011770842	0.005195039	0.005836971	0.030300456	0.011397451	0.019803161	0.000000000	0.000000000	0.230896865	0.391879727	AT3G54460	AT3G54460
AT4G13040	0.553191474	0.035265618	0.039379131	0.036986217	0.010723110	0.000000000	0.000000000	0.000000000	0.000000000	0.103130348	0.327707050	AT4G13040	AT4G13040
RING1	0.306628935	0.006043877	0.000000000	0.000000000	0.000000000	0.000000000	0.013431320	0.000000000	0.000000000	0.097875315	0.189278424	AT5G10380	RING1
BPC7	0.371493013	0.029533810	0.000000000	0.000000000	0.003824092	0.000000000	0.005392758	0.000000000	0.000000000	0.144092060	0.188650294	AT2G35550	BPC7
NF-YC12	0.111223991	0.000000000	0.000000000	0.007722008	0.000000000	0.000000000	0.005860963	0.000000000	0.000000000	0.017402388	0.080238632	AT5G38140	NF-YC12
AT2G33550	0.081155475	0.000000000	0.004686656	0.000000000	0.003824092	0.000000000	0.000000000	0.000000000	0.000000000	0.009696242	0.062948486	AT2G33550	AT2G33550
AT1G18560	0.074964285	0.000000000	0.003293898	0.000000000	0.002868069	0.000000000	0.009032084	0.000000000	0.006275523	0.010779031	0.042715680	AT1G18560	AT1G18560
AT4G36050	0.060859671	0.000000000	0.000000000	0.000000000	0.006692161	0.000000000	0.010144816	0.000000000	0.004456859	0.000000000	0.039565836	AT4G36050	AT4G36050
BEH1	0.031272143	0.000000000	0.005961299	0.000000000	0.002868069	0.000000000	0.000000000	0.000000000	0.000000000	0.005921977	0.016520798	AT3G50750	BEH1
AT5G08520	0.008843193	0.000000000	0.000000000	0.000000000	0.003824092	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.005019101	AT5G08520	AT5G08520

options(repr.plot.width=6, repr.plot.height=4)
ggplot(col_rank[1:10,], aes(x=reorder(GeneName, col, decreasing = FALSE),  y=col)) + geom_point(size=4)+ 
  labs(title="Columella-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(col_rank,"Columella_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- col_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Columella ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Add tissue level

bc_rank <- bc_rank %>% mutate(ground=cor+end, epi=atri+tri, stele=per+pro+xyl+phl, epilrc=atri+tri+lrc, rc=lrc+col)

head(bc_rank)

A data.frame: 6 x 17

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
BZIP9	31.42213	0.000000	0.000000	0.00000000	0.005736138	8.55316613	11.88088107	0.02003315	10.9623184	0.000000	0.000000	AT5G24800	0.005736138	0.000000	31.41639871	0.00000	0.000000
AT3G43430	29.05965	0.000000	0.000000	0.00544388	0.029360833	9.20656526	11.24626128	1.83863137	6.7333880	0.000000	0.000000	AT3G43430	0.034804713	0.000000	29.02484588	0.00000	0.000000
PLT1	23.97514	3.940608	0.000000	2.00953127	1.492057686	0.05699842	0.00000000	0.00000000	0.0000000	9.148008	7.327940	AT3G20840	3.501588961	3.940608	0.05699842	13.08862	16.475948
HAT7	33.13977	6.200914	5.547329	8.04274907	5.877125010	1.24427942	0.00000000	0.00000000	0.0122843	4.284922	1.930171	AT5G15150	13.919874084	11.748243	1.25656372	16.03317	6.215093
MYB36	20.57418	0.000000	0.000000	6.29093689	10.748544272	3.53469871	0.00000000	0.00000000	0.0000000	0.000000	0.000000	AT5G57620	17.039481158	0.000000	3.53469871	0.00000	0.000000
GATA2	33.12273	6.623792	6.596557	2.46621779	2.051664424	0.80375905	0.03881894	0.09406244	0.1559529	12.103509	2.188393	AT2G45050	4.517882216	13.220349	1.09259336	25.32386	14.291902

Ground Tissue

ground_rank <- bc_rank[which(bc_rank$ground*2 > bc_rank$all),]%>% arrange(desc(ground))
ground_rank <- ground_rank[-c(match(rownames(cor_rank), rownames(ground_rank)),match(rownames(end_rank), rownames(ground_rank))),]
ground_rank$GeneName <- rownames(ground_rank)

ground_rank

A data.frame: 58 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
MYB12	23.6925714	0.086429636	0.116639239	7.788192391	5.78330037	1.929749529	0.516205906	3.371248525	1.210403057	0.110529735	2.779872975	AT2G47460	13.57149276	0.203068875	7.027607017	0.31359861	2.890402710	MYB12
MYB3	11.8841880	0.516196596	0.089796448	2.733348149	5.59048060	2.347020632	0.023860054	0.392757518	0.051272502	0.135677055	0.003778406	AT1G22640	8.32382874	0.605993044	2.814910706	0.74167010	0.139455462	MYB3
JKD	8.4732071	0.000000000	0.000000000	3.707652355	2.97524102	0.046405172	0.000000000	0.000000000	0.000000000	0.000000000	1.743908530	AT5G03150	6.68289338	0.000000000	0.046405172	0.00000000	1.743908530	JKD
AN3	9.1822731	1.164651915	0.192213903	2.337373113	3.15346845	2.100233534	0.058317549	0.000000000	0.058683307	0.112312266	0.005019101	AT5G28640	5.49084156	1.356865818	2.217234390	1.46917808	0.117331367	AN3
ARR3	8.5913166	0.277809106	0.042398606	2.342944255	3.13353902	1.024916677	0.292536040	0.000000000	0.761437377	0.227870782	0.487864717	AT1G59940	5.47648327	0.320207712	2.078890094	0.54807849	0.715735499	ARR3
AtHB23	8.8753093	0.501488974	0.374681517	3.154663839	1.83622525	1.774905009	0.759227164	0.123371046	0.238366599	0.047751144	0.064628773	AT1G26960	4.99088909	0.876170491	2.895869819	0.92392163	0.112379917	AtHB23
GATA16	9.1755332	0.000000000	0.000000000	2.717450549	1.93427924	1.963803057	0.385471434	0.600895613	0.024737577	0.274582712	1.274312995	AT5G49300	4.65172978	0.000000000	2.974907680	0.27458271	1.548895708	GATA16
ULT1	7.0016708	0.112700338	0.004686656	2.229074863	2.07787649	1.582433859	0.147766912	0.195664304	0.032078314	0.394481296	0.224907778	AT4G28190	4.30695135	0.117386994	1.957943389	0.51186829	0.619389073	ULT1
HSFB4	5.2735423	0.120861144	0.000000000	1.634055563	2.58265054	0.381517308	0.423129431	0.099979596	0.031348729	0.000000000	0.000000000	AT1G46264	4.21670610	0.120861144	0.935975064	0.12086114	0.000000000	HSFB4
ABS2	7.5430799	0.116787466	0.027995342	2.404733477	1.38370900	0.157625686	0.031317493	0.544361549	0.263006040	0.397556402	2.215987470	AT2G36080	3.78844248	0.144782808	0.996310768	0.54233921	2.613543872	ABS2
AT5G57150	6.2922314	0.150126218	0.000000000	0.974717001	2.56868894	0.666176661	0.326689893	0.000000000	1.395297246	0.107660702	0.102874696	AT5G57150	3.54340595	0.150126218	2.388163800	0.25778692	0.210535398	AT5G57150
MNP	5.7890576	0.455490525	0.076023492	1.575465392	1.53636438	0.261655240	0.085935005	1.649952441	0.148171124	0.000000000	0.000000000	AT3G50870	3.11182978	0.531514017	2.145713810	0.53151402	0.000000000	MNP
HB5	4.3101311	0.000000000	0.000000000	1.631499417	0.90681682	0.618155504	0.674749557	0.004983519	0.467666517	0.000000000	0.006259796	AT5G65310	2.53831624	0.000000000	1.765555097	0.00000000	0.006259796	HB5
AT3G24120	2.9748807	0.000000000	0.000000000	1.318839111	0.94921303	0.443322301	0.199393514	0.000000000	0.046574013	0.000000000	0.017538693	AT3G24120	2.26805214	0.000000000	0.689289829	0.00000000	0.017538693	AT3G24120
AT3G23690	4.4070459	0.174698758	0.007929803	0.704941693	1.53239725	0.800609034	0.425340033	0.207459739	0.515680653	0.035451259	0.002537712	AT3G23690	2.23733895	0.182628561	1.949089458	0.21807982	0.037988971	AT3G23690
AT1G63100	3.1124480	0.465963596	0.112858909	1.432843966	0.79925969	0.086892337	0.000000000	0.000000000	0.000000000	0.214629549	0.000000000	AT1G63100	2.23210365	0.578822505	0.086892337	0.79345205	0.214629549	AT1G63100
PRMT3	4.0160072	0.535116699	0.380272681	1.087282543	1.10717393	0.255490804	0.000000000	0.000000000	0.000000000	0.484268957	0.166401584	AT3G12270	2.19445648	0.915389380	0.255490804	1.39965834	0.650670542	PRMT3
AT3G61420	3.5419499	1.163565877	0.161087741	1.539386008	0.62316908	0.014899816	0.022547350	0.000000000	0.000000000	0.000000000	0.017294004	AT3G61420	2.16255508	1.324653618	0.037447166	1.32465362	0.017294004	AT3G61420
RGL3	3.8656758	0.148167809	0.071988745	1.829941208	0.17182469	0.000000000	0.005302323	0.040598935	0.000000000	0.333055643	1.264796477	AT5G17490	2.00176590	0.220156554	0.045901257	0.55321220	1.597852120	RGL3
AT4G36860	2.9275705	0.033367005	0.000000000	0.691306976	0.94922066	0.055919455	0.046380543	0.537992075	0.198786042	0.142671389	0.271926374	AT4G36860	1.64052763	0.033367005	0.839078116	0.17603839	0.414597763	AT4G36860
HB6	2.6007464	0.315425451	0.061080034	0.912825103	0.52862454	0.134608602	0.087486844	0.000000000	0.110169804	0.138287349	0.312238711	AT2G22430	1.44144964	0.376505485	0.332265250	0.51479283	0.450526060	HB6
PS1	2.6530463	0.546168704	0.056755736	1.005039809	0.38799668	0.020787558	0.000000000	0.000000000	0.000000000	0.559072001	0.077225845	AT1G34355	1.39303649	0.602924440	0.020787558	1.16199644	0.636297846	PS1
COL4	2.4169318	0.063200804	0.000000000	0.966848642	0.41908013	0.143167613	0.090663777	0.002508272	0.000000000	0.165613884	0.565848640	AT5G24930	1.38592877	0.063200804	0.236339662	0.22881469	0.731462524	COL4
AT4G00940	2.3993025	0.000000000	0.000000000	0.203935552	1.04918249	0.005688667	0.035019085	1.105476677	0.000000000	0.000000000	0.000000000	AT4G00940	1.25311804	0.000000000	1.146184429	0.00000000	0.000000000	AT4G00940
SUVR2	2.3134711	0.325030777	0.000000000	0.703323606	0.50964237	0.120001232	0.012475395	0.000000000	0.035791555	0.488670060	0.118536062	AT5G43990	1.21296597	0.325030777	0.168268182	0.81370084	0.607206122	SUVR2
WRKY69	1.7522534	0.040652356	0.258101605	0.870534238	0.13749881	0.000000000	0.000000000	0.000000000	0.004497468	0.047418217	0.393550683	AT3G58710	1.00803305	0.298753961	0.004497468	0.34617218	0.440968900	WRKY69
ZFN1	1.9539416	0.038933391	0.000000000	0.727648297	0.26632704	0.068901110	0.064464235	0.000000000	0.048401262	0.254359319	0.484906984	AT3G02830	0.99397534	0.038933391	0.181766607	0.29329271	0.739266304	ZFN1
LCL1	1.7329580	0.019018672	0.000000000	0.303982413	0.60939017	0.232686240	0.341514227	0.041771769	0.069309790	0.000000000	0.115284710	AT5G02840	0.91337258	0.019018672	0.685282026	0.01901867	0.115284710	LCL1
3xHMG-box1	1.5450654	0.196338140	0.111861405	0.484529348	0.42847205	0.111876888	0.000000000	0.000000000	0.000000000	0.171555371	0.040432152	AT4G11080	0.91300140	0.308199545	0.111876888	0.47975492	0.211987524	3xHMG-box1
bZIP52	1.6245433	0.046008259	0.015014407	0.432320147	0.47774401	0.101767317	0.107019651	0.035400939	0.181669729	0.161749449	0.065849407	AT1G06850	0.91006416	0.061022666	0.425857636	0.22277212	0.227598857	bZIP52
APRR2	1.7714481	0.000000000	0.000000000	0.485789146	0.41584802	0.102986446	0.188330026	0.000000000	0.535460274	0.000000000	0.043034202	AT4G18020	0.90163716	0.000000000	0.826776745	0.00000000	0.043034202	APRR2
AT5G58900	1.3106062	0.051104408	0.110660095	0.597992303	0.22140808	0.000000000	0.007418725	0.007524815	0.249202773	0.016609935	0.048685091	AT5G58900	0.81940038	0.161764502	0.264146314	0.17837444	0.065295026	AT5G58900
AT5G41920	1.3986689	0.000000000	0.000000000	0.109305439	0.63710435	0.652259084	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G41920	0.74640979	0.000000000	0.652259084	0.00000000	0.000000000	AT5G41920
AT5G58620	1.4402432	0.086608813	0.043989938	0.043670939	0.70041497	0.250150241	0.263850678	0.000000000	0.041948843	0.009608780	0.000000000	AT5G58620	0.74408591	0.130598751	0.555949762	0.14020753	0.009608780	AT5G58620
AT1G16640	1.3064148	0.356329074	0.121084627	0.325830297	0.34747815	0.087930857	0.000000000	0.000000000	0.000000000	0.067761787	0.000000000	AT1G16640	0.67330844	0.477413701	0.087930857	0.54517549	0.067761787	AT1G16640
AT3G61180	1.1727461	0.036857537	0.081297090	0.351443250	0.26853951	0.093960844	0.204021898	0.061583289	0.068726565	0.000000000	0.006316118	AT3G61180	0.61998276	0.118154627	0.428292596	0.11815463	0.006316118	AT3G61180
GATA27	1.0010143	0.013811893	0.110196189	0.239503268	0.38018587	0.042672891	0.119367312	0.012574383	0.067724536	0.000000000	0.014977929	AT5G47140	0.61968914	0.124008082	0.242339122	0.12400808	0.014977929	GATA27
AT5G23930	1.1554572	0.341155309	0.000000000	0.217370051	0.40154208	0.096015492	0.000000000	0.000000000	0.000000000	0.099374294	0.000000000	AT5G23930	0.61891213	0.341155309	0.096015492	0.44052960	0.099374294	AT5G23930
ETR2	0.8810476	0.025868306	0.000000000	0.412857161	0.19745438	0.000000000	0.007418725	0.000000000	0.000000000	0.000000000	0.237449024	AT3G23150	0.61031154	0.025868306	0.007418725	0.02586831	0.237449024	ETR2
MYB14	1.1598497	0.080736391	0.000000000	0.434523451	0.16433452	0.112716303	0.167740974	0.100861831	0.000000000	0.000000000	0.098936221	AT2G31180	0.59885797	0.080736391	0.381319108	0.08073639	0.098936221	MYB14
AT2G18850	1.0043199	0.265798096	0.049790986	0.440368143	0.13386258	0.022468984	0.008297122	0.007557840	0.000000000	0.034516230	0.041659886	AT2G18850	0.57423072	0.315589081	0.038323945	0.35010531	0.076176116	AT2G18850
AT5G66270	1.0381664	0.253435414	0.089538398	0.223487935	0.30412381	0.066242104	0.000000000	0.000000000	0.000000000	0.069823998	0.031514723	AT5G66270	0.52761175	0.342973812	0.066242104	0.41279781	0.101338721	AT5G66270
MYB70	0.6756527	0.004093299	0.010766070	0.118862041	0.31425952	0.070259808	0.006930481	0.000000000	0.047744553	0.033848569	0.068888390	AT2G23290	0.43312156	0.014859369	0.124934843	0.04870794	0.102736959	MYB70
ARR9	0.7985496	0.029599649	0.047370685	0.096343680	0.32658562	0.036901919	0.101063431	0.111231055	0.023815852	0.000000000	0.025637763	AT3G57040	0.42292930	0.076970334	0.273012257	0.07697033	0.025637763	ARR9
ING1	0.6800806	0.009851456	0.024933884	0.024281286	0.32972088	0.036606105	0.048414902	0.016287255	0.123860308	0.007576104	0.058548459	AT3G24010	0.35400216	0.034785341	0.225168570	0.04236145	0.066124563	ING1
AT5G58280	0.6589519	0.095722637	0.000000000	0.246624049	0.09621251	0.023448229	0.000000000	0.000000000	0.000000000	0.128355047	0.068589410	AT5G58280	0.34283656	0.095722637	0.023448229	0.22407768	0.196944457	AT5G58280
AHBP-1B	0.5457062	0.000000000	0.014891257	0.109522503	0.19195473	0.048301174	0.025170840	0.011336759	0.070534984	0.025764625	0.048229351	AT5G06950	0.30147723	0.014891257	0.155343757	0.04065588	0.073993975	AHBP-1B
ZFN3	0.4990681	0.040070362	0.120583045	0.129386842	0.16132178	0.000000000	0.000000000	0.000000000	0.038908574	0.000000000	0.008797508	AT5G16540	0.29070862	0.160653407	0.038908574	0.16065341	0.008797508	ZFN3
AT5G43530	0.4767440	0.080377787	0.078633741	0.108588739	0.15390481	0.055238945	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	AT5G43530	0.26249355	0.159011527	0.055238945	0.15901153	0.000000000	AT5G43530
EPR1	0.4293309	0.004093299	0.000000000	0.126930325	0.13466900	0.000000000	0.010649917	0.080085697	0.020145733	0.024993840	0.027763127	AT1G18330	0.26159932	0.004093299	0.110881346	0.02908714	0.052756966	EPR1
AGL65	0.4719325	0.056449854	0.000000000	0.161679530	0.09215518	0.037786153	0.005392758	0.000000000	0.000000000	0.078036838	0.040432152	AT1G18750	0.25383471	0.056449854	0.043178911	0.13448669	0.118468990	AGL65
AT5G07810	0.3887479	0.035257420	0.025398776	0.148957880	0.09997634	0.000000000	0.000000000	0.000000000	0.000000000	0.049240538	0.029916967	AT5G07810	0.24893422	0.060656195	0.000000000	0.10989673	0.079157505	AT5G07810
NSI	0.4222833	0.032764253	0.068238176	0.068522550	0.14313237	0.055236231	0.018131918	0.004983519	0.022420414	0.000000000	0.008853830	AT1G32070	0.21165492	0.101002429	0.100772083	0.10100243	0.008853830	NSI
AT2G46735	0.3031413	0.028737266	0.038328706	0.080708960	0.07751234	0.000000000	0.044635983	0.002508272	0.015654122	0.000000000	0.015055632	AT2G46735	0.15822130	0.067065972	0.062798376	0.06706597	0.015055632	AT2G46735
AT5G05130	0.1370665	0.006619989	0.000000000	0.041166101	0.05140327	0.000000000	0.000000000	0.000000000	0.000000000	0.017414117	0.020462979	AT5G05130	0.09256937	0.006619989	0.000000000	0.02403411	0.037877096	AT5G05130
AT5G13920	0.1258854	0.000000000	0.015803819	0.008117677	0.05786379	0.017455709	0.017749304	0.000000000	0.008895096	0.000000000	0.000000000	AT5G13920	0.06598147	0.015803819	0.044100108	0.01580382	0.000000000	AT5G13920
AT5G47790	0.1216455	0.000000000	0.009187834	0.008311856	0.05532825	0.000000000	0.007418725	0.008795463	0.025247070	0.003577863	0.003778406	AT5G47790	0.06364010	0.009187834	0.041461258	0.01276570	0.007356270	AT5G47790
AT2G17150	0.1140957	0.000000000	0.000000000	0.008311856	0.05229974	0.000000000	0.016450647	0.000000000	0.006095925	0.000000000	0.030937564	AT2G17150	0.06061159	0.000000000	0.022546572	0.00000000	0.030937564	AT2G17150

options(repr.plot.width=6, repr.plot.height=4)
ggplot(ground_rank[1:10,], aes(x=reorder(GeneName, ground, decreasing = FALSE),  y=ground)) + geom_point(size=4)+ 
  labs(title="Ground Tissue-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(ground_rank,"Ground_Tissue_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- ground_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Ground Tissue ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Epidermis

epi_rank <- bc_rank[which(bc_rank$epi*2 > bc_rank$all),]%>% arrange(desc(epi))
epi_rank <- epi_rank[-c(match(rownames(atri_rank), rownames(epi_rank)),match(rownames(tri_rank), rownames(epi_rank))),]
epi_rank$GeneName <- rownames(epi_rank)

epi_rank

A data.frame: 50 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
WRKY75	14.27247625	4.50121971	6.35682293	0.105330391	0.020802243	0.000000000	0.000000000	0.000000000	0.00000000	0.562987445	2.725313541	AT5G13080	0.126132634	10.85804263	0.000000000	11.42103008	3.288300987	WRKY75
KDR	11.62635997	5.16954066	4.61693369	0.687132713	0.421165730	0.122605397	0.000000000	0.000000000	0.00000000	0.467248858	0.141732932	AT1G26945	1.108298444	9.78647434	0.122605397	10.25372320	0.608981789	KDR
TGA10	11.88448968	4.95436167	4.44299660	1.568650672	0.855081570	0.000000000	0.000000000	0.000000000	0.00000000	0.063399162	0.000000000	AT5G06839	2.423732242	9.39735827	0.000000000	9.46075743	0.063399162	TGA10
ATS	16.50726004	5.44112099	3.30585225	0.968613101	0.707613234	0.051316603	0.000000000	0.000000000	0.00000000	5.946192427	0.086551437	AT5G42630	1.676226335	8.74697324	0.051316603	14.69316566	6.032743865	ATS
ATMYC1	9.78145051	3.77655716	4.62113827	0.678339208	0.609503007	0.095912867	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	AT4G00480	1.287842216	8.39769543	0.095912867	8.39769543	0.000000000	ATMYC1
ATHB13	12.94066661	3.43823012	4.68668673	1.275819260	1.039918586	1.305370636	0.284257457	0.000000000	0.11530623	0.742912093	0.052165503	AT1G69780	2.315737846	8.12491684	1.704934322	8.86782894	0.795077596	ATHB13
ARR5	8.89376453	3.90153218	3.60543610	0.323971285	0.220153526	0.004604770	0.036677828	0.028683106	0.04366444	0.315386196	0.413655090	AT3G48100	0.544124811	7.50696828	0.113630146	7.82235448	0.729041287	ARR5
EGL3	9.21190572	3.31146732	2.93164385	1.616857995	1.213590291	0.138346265	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	AT1G63650	2.830448286	6.24311117	0.138346265	6.24311117	0.000000000	EGL3
RSL1	7.02070379	2.79503956	3.39167865	0.391179276	0.442806304	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	AT5G37800	0.833985580	6.18671821	0.000000000	6.18671821	0.000000000	RSL1
TRY	9.06259414	4.42583642	1.73861316	1.326472988	1.187753102	0.065521148	0.000000000	0.000000000	0.02658114	0.291816191	0.000000000	AT5G53200	2.514226090	6.16444958	0.092102284	6.45626577	0.291816191	TRY
WRKY65	7.76971222	2.97579985	2.87049627	0.065232345	0.198280979	0.000000000	0.000000000	0.000000000	0.00000000	1.174474140	0.485428646	AT1G29280	0.263513325	5.84629611	0.000000000	7.02077025	1.659902786	WRKY65
NFL	8.51771460	2.69458888	2.66075182	1.211807252	0.957362456	0.000000000	0.000000000	0.000000000	0.00000000	0.766809048	0.226395146	AT5G65640	2.169169709	5.35534070	0.000000000	6.12214975	0.993204194	NFL
WRKY31	4.08517147	1.79205116	1.93379457	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.00000000	0.113247738	0.246078001	AT4G22070	0.000000000	3.72584574	0.000000000	3.83909347	0.359325739	WRKY31
ARR4	6.72900087	2.21785353	1.49870609	0.352242975	0.259826637	0.032473533	0.093496702	0.902702934	0.36938618	0.588931939	0.413380355	AT1G10470	0.612069613	3.71655962	1.398059346	4.30549156	1.002312294	ARR4
BNQ3	5.05983326	2.39743980	0.92866023	0.007722008	0.000000000	0.000000000	0.000000000	0.000000000	0.00000000	1.714071366	0.011939866	AT3G47710	0.007722008	3.32610002	0.000000000	5.04017139	1.726011231	BNQ3
tny	5.94109200	0.07049984	2.92972090	2.845054038	0.009767087	0.003045749	0.083004383	0.000000000	0.00000000	0.000000000	0.000000000	AT5G25810	2.854821125	3.00022074	0.086050132	3.00022074	0.000000000	tny
AT3G14740	4.63360687	1.48628662	1.02910395	0.052439006	0.080853310	0.857512895	0.234059451	0.311910070	0.15857334	0.405268899	0.017599319	AT3G14740	0.133292317	2.51539058	1.562055760	2.92065947	0.422868218	AT3G14740
AT5G44260	4.24918637	0.51514067	1.90034095	0.363876159	0.544155781	0.074447267	0.000000000	0.000000000	0.00000000	0.509355564	0.341869975	AT5G44260	0.908031940	2.41548162	0.074447267	2.92483718	0.851225540	AT5G44260
WRI1	3.72151672	1.71959721	0.56962058	0.258343579	0.186074488	0.088291335	0.000000000	0.000000000	0.00000000	0.812360475	0.087229041	AT3G54320	0.444418067	2.28921780	0.088291335	3.10157827	0.899589516	WRI1
AT4G26810	3.39210973	1.24137412	0.94041031	0.222932437	0.261870227	0.495560627	0.224601616	0.000000000	0.00000000	0.005360395	0.000000000	AT4G26810	0.484802664	2.18178443	0.720162242	2.18714482	0.005360395	AT4G26810
NF-YC13	3.69251878	1.30516262	0.84404440	0.387994507	0.699191452	0.002982303	0.000000000	0.333651367	0.00000000	0.119492121	0.000000000	AT5G43250	1.087185959	2.14920703	0.336633670	2.26869915	0.119492121	NF-YC13
OFP13	2.83417196	0.77262982	1.23976071	0.387420971	0.256171992	0.008669323	0.003185921	0.000000000	0.00000000	0.093051719	0.073281496	AT5G04820	0.643592963	2.01239053	0.011855244	2.10544225	0.166333215	OFP13
HDG7	3.31341935	1.57034339	0.43963925	0.530950184	0.407675480	0.000000000	0.000000000	0.000000000	0.00000000	0.325434691	0.039376358	AT5G52170	0.938625664	2.00998263	0.000000000	2.33541732	0.364811049	HDG7
HRS1	2.75985065	1.31214046	0.59487847	0.298884996	0.519438376	0.027089623	0.007418725	0.000000000	0.00000000	0.000000000	0.000000000	AT1G13300	0.818323372	1.90701893	0.034508348	1.90701893	0.000000000	HRS1
HMGB2	2.75746694	0.62455463	1.07055053	0.175173093	0.244648860	0.223163133	0.165502300	0.020099199	0.19185287	0.008576734	0.033345588	AT1G20693	0.419821953	1.69510516	0.600617505	1.70368189	0.041922322	HMGB2
AT5G42700	2.66806247	1.14692762	0.23198585	0.400151095	0.362753222	0.222492104	0.000000000	0.243623750	0.00000000	0.060128828	0.000000000	AT5G42700	0.762904317	1.37891347	0.466115854	1.43904229	0.060128828	AT5G42700
MED6	1.44791533	0.47208033	0.53292250	0.024353030	0.187227487	0.076984211	0.010724510	0.020132223	0.04896930	0.041097619	0.033424122	AT3G21350	0.211580518	1.00500283	0.156810246	1.04610045	0.074521741	MED6
ORC1B	1.48364884	0.53621302	0.41805620	0.000000000	0.052070705	0.159463448	0.012356546	0.262605753	0.00000000	0.042883156	0.000000000	AT4G12620	0.052070705	0.95426923	0.434425747	0.99715239	0.042883156	ORC1B
LOL2	1.19935231	0.39396708	0.48163750	0.032759566	0.000000000	0.046119087	0.020831941	0.006320216	0.01068484	0.159327474	0.047704601	AT4G21610	0.032759566	0.87560458	0.083956084	1.03493206	0.207032076	LOL2
HFR1	1.49598207	0.38654756	0.43671310	0.488623933	0.184097466	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	AT1G02340	0.672721399	0.82326067	0.000000000	0.82326067	0.000000000	HFR1
HDG1	1.23260977	0.46529083	0.24272326	0.080984194	0.000000000	0.000000000	0.000000000	0.000000000	0.00000000	0.390817231	0.052794262	AT3G61150	0.080984194	0.70801408	0.000000000	1.09883131	0.443611493	HDG1
MBD6	0.95978369	0.28416110	0.42150179	0.035529649	0.008604207	0.117048819	0.005302323	0.026321159	0.01059339	0.044948472	0.005772775	AT5G59380	0.044133855	0.70566289	0.159265694	0.75061136	0.050721247	MBD6
NF-YC10	1.13370071	0.25995401	0.43410473	0.100555977	0.193202483	0.020012672	0.003185921	0.000000000	0.01241380	0.036939252	0.073331868	AT1G07980	0.293758460	0.69405874	0.035612395	0.73099799	0.110271120	NF-YC10
RSZ22a	1.18417349	0.41119117	0.27535727	0.000000000	0.055363523	0.100303568	0.022224698	0.016386327	0.11485350	0.158637357	0.029856069	AT2G24590	0.055363523	0.68654844	0.253768098	0.84518580	0.188493427	RSZ22a
WRKY3	0.93124326	0.31051817	0.24381423	0.000000000	0.002868069	0.009137246	0.027389773	0.000000000	0.04343970	0.131535984	0.162540092	AT2G03340	0.002868069	0.55433239	0.079966716	0.68586838	0.294076077	WRKY3
AT5G66770	1.09068944	0.45600043	0.09499634	0.135519279	0.074234484	0.007721640	0.012415970	0.037543358	0.01338920	0.189002563	0.069866180	AT5G66770	0.209753764	0.55099677	0.071070168	0.73999933	0.258868743	AT5G66770
AT3G57480	0.81065314	0.20361446	0.28739796	0.006228522	0.034553874	0.002863808	0.006360524	0.126151523	0.00000000	0.082666118	0.060816364	AT3G57480	0.040782397	0.49101241	0.135375855	0.57367853	0.143482482	AT3G57480
PUX2	0.71372439	0.28674622	0.17953620	0.035151157	0.105239453	0.000000000	0.027878112	0.006320216	0.01658114	0.005966671	0.050305224	AT2G01650	0.140390610	0.46628242	0.050779464	0.47224909	0.056271895	PUX2
NPR1	0.72784235	0.23596400	0.15667550	0.049562669	0.057509664	0.004849074	0.006904364	0.000000000	0.00000000	0.077267382	0.139109693	AT1G64280	0.107072333	0.39263950	0.011753438	0.46990688	0.216377075	NPR1
AT2G01060	0.60397090	0.09311377	0.29945681	0.028485217	0.055838837	0.020628326	0.000000000	0.002508272	0.02401195	0.042542271	0.037385446	AT2G01060	0.084324054	0.39257058	0.047148546	0.43511285	0.079927717	AT2G01060
AT1G76110	0.52155936	0.14219343	0.22133878	0.059593820	0.055629424	0.037292296	0.005511606	0.000000000	0.00000000	0.000000000	0.000000000	AT1G76110	0.115223244	0.36353221	0.042803902	0.36353221	0.000000000	AT1G76110
ATMAK3	0.56549872	0.20148093	0.14449258	0.000000000	0.033498370	0.063714866	0.029090761	0.026320342	0.02126596	0.040454391	0.005180522	AT2G38130	0.033498370	0.34597351	0.140391929	0.38642790	0.045634913	ATMAK3
CHR8	0.45170578	0.12096937	0.19871424	0.000000000	0.025413486	0.000000000	0.032010385	0.000000000	0.02654383	0.000000000	0.048054472	AT2G18760	0.025413486	0.31968361	0.058554211	0.31968361	0.048054472	CHR8
AT5G28300	0.44283743	0.18600653	0.12138746	0.086338384	0.004780115	0.000000000	0.000000000	0.000000000	0.02674835	0.000000000	0.017576592	AT5G28300	0.091118498	0.30739399	0.026748349	0.30739399	0.017576592	AT5G28300
WRKY54	0.36001476	0.14209582	0.08107857	0.000000000	0.000000000	0.000000000	0.019201486	0.000000000	0.00000000	0.112753568	0.004885314	AT2G40750	0.000000000	0.22317439	0.019201486	0.33592796	0.117638881	WRKY54
EMB2219	0.33585476	0.12743751	0.05277167	0.022064379	0.040583082	0.025158402	0.009630455	0.000000000	0.00000000	0.036432218	0.021777039	AT2G21710	0.062647461	0.18020918	0.034788857	0.21664140	0.058209257	EMB2219
AT2G37000	0.17297214	0.05694063	0.08066012	0.031252544	0.000000000	0.000000000	0.004118849	0.000000000	0.00000000	0.000000000	0.000000000	AT2G37000	0.031252544	0.13760075	0.004118849	0.13760075	0.000000000	AT2G37000
FRF1	0.19864460	0.04216337	0.08087802	0.005836971	0.028956831	0.000000000	0.026028442	0.000000000	0.01478097	0.000000000	0.000000000	AT3G59470	0.034793803	0.12304139	0.040809407	0.12304139	0.000000000	FRF1
AT3G51180	0.09952575	0.04051087	0.02509337	0.000000000	0.028619193	0.000000000	0.005302323	0.000000000	0.00000000	0.000000000	0.000000000	AT3G51180	0.028619193	0.06560423	0.005302323	0.06560423	0.000000000	AT3G51180
AT1G61960	0.05563907	0.02294639	0.02089916	0.000000000	0.002868069	0.000000000	0.000000000	0.000000000	0.00000000	0.008925457	0.000000000	AT1G61960	0.002868069	0.04384555	0.000000000	0.05277100	0.008925457	AT1G61960

options(repr.plot.width=6, repr.plot.height=4)
ggplot(epi_rank[1:10,], aes(x=reorder(GeneName, epi, decreasing = FALSE),  y=epi)) + geom_point(size=4)+ 
  labs(title="Epidermis-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(epi_rank,"Epidermis_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- epi_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Epidermis ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Epidermis + LRC

epilrc_rank <- bc_rank[which(bc_rank$epilrc*2 > bc_rank$all),]%>% arrange(desc(epilrc))
epilrc_rank <- epilrc_rank[-c(match(rownames(atri_rank), rownames(epilrc_rank)),match(rownames(tri_rank), rownames(epilrc_rank)),match(rownames(lrc_rank), rownames(epilrc_rank))),]
epilrc_rank$GeneName <- rownames(epilrc_rank)

epilrc_rank

A data.frame: 189 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
GATA2	33.122726	6.623792	6.596556930	2.4662178	2.051664424	0.80375905	0.038818942	0.094062436	0.155952926	12.10350859	2.18839297	AT2G45050	4.5178822	13.220349	1.092593358	25.323857	14.29190157	GATA2
NAI1	26.293062	4.531465	2.427442200	0.4453633	0.205157772	0.00000000	0.000000000	0.000000000	0.000000000	10.31475737	8.36887560	AT2G22770	0.6505211	6.958907	0.000000000	17.273665	18.68363298	NAI1
CRF3	23.156426	3.728132	2.509432523	1.5634632	1.455509716	0.22797244	0.046967234	0.000000000	0.007808814	9.41462626	4.20251415	AT5G53290	3.0189729	6.237564	0.282748493	15.652190	13.61714041	CRF3
ATS	16.507260	5.441121	3.305852246	0.9686131	0.707613234	0.05131660	0.000000000	0.000000000	0.000000000	5.94619243	0.08655144	AT5G42630	1.6762263	8.746973	0.051316603	14.693166	6.03274386	ATS
WRKY9	16.059040	3.419059	3.623664192	0.0000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	7.33266559	1.68365192	AT1G68150	0.0000000	7.042723	0.000000000	14.375388	9.01631751	WRKY9
WER	21.502304	4.805167	3.023849655	2.2988231	1.929804951	1.60818052	0.000000000	0.000000000	0.000000000	6.47458657	1.36189276	AT5G14750	4.2286280	7.829016	1.608180520	14.303603	7.83647933	WER
BRON	21.210029	4.113443	1.541404242	1.7786247	0.797459038	0.02949052	0.000000000	0.000000000	0.000000000	8.37774914	4.57185874	AT1G75710	2.5760837	5.654847	0.029490525	14.032596	12.94960788	BRON
CRF2	26.776206	4.253729	2.624377101	2.0929756	1.946945888	2.86595170	0.227652705	0.076617017	1.680974990	7.01682963	3.99015150	AT4G23750	4.0399215	6.878107	4.851196406	13.894936	11.00698114	CRF2
BT2	21.465456	3.193818	1.318831580	0.1865704	0.008604207	0.00000000	0.000000000	0.000000000	0.000000000	8.80460777	7.95302395	AT3G48360	0.1951746	4.512650	0.000000000	13.317258	16.75763172	BT2
PLT1	23.975143	3.940608	0.000000000	2.0095313	1.492057686	0.05699842	0.000000000	0.000000000	0.000000000	9.14800761	7.32793997	AT3G20840	3.5015890	3.940608	0.056998421	13.088616	16.47594759	PLT1
AT1G36060	19.317669	3.288870	0.911066727	0.4154557	0.417050853	0.01438478	0.000000000	0.000000000	0.000000000	7.43126331	6.83957810	AT1G36060	0.8325066	4.199936	0.014384784	11.631200	14.27084142	AT1G36060
WRKY75	14.272476	4.501220	6.356822928	0.1053304	0.020802243	0.00000000	0.000000000	0.000000000	0.000000000	0.56298745	2.72531354	AT5G13080	0.1261326	10.858043	0.000000000	11.421030	3.28830099	WRKY75
GATA4	19.762788	5.005152	3.160243703	2.9003810	3.408120604	0.22564199	0.000000000	0.188124872	0.033494724	2.96550233	1.87612624	AT3G60530	6.3085016	8.165396	0.447261590	11.130898	4.84162856	GATA4
TMO7	20.540646	5.343586	2.778498293	4.3047024	1.404510625	0.69068801	0.000000000	0.000000000	0.007790552	2.93997637	3.07089432	AT1G74500	5.7092130	8.122084	0.698478562	11.062060	6.01087069	TMO7
SMB	18.647620	3.795156	0.154934264	1.3885615	0.208595675	0.00000000	0.000000000	0.000000000	0.000000000	6.95364674	6.14672573	AT1G79580	1.5971572	3.950090	0.000000000	10.903737	13.10037247	SMB
WRKY17	15.090675	3.305227	1.357150829	1.1777238	0.737343969	0.32690649	0.029931039	0.694344583	0.261731502	6.01333696	1.18697873	AT2G24570	1.9150678	4.662378	1.312913611	10.675715	7.20031569	WRKY17
AT1G26680	17.564236	2.110786	0.000000000	1.4957776	0.073110164	0.00000000	0.000000000	0.000000000	0.000000000	8.56440431	5.32015736	AT1G26680	1.5688878	2.110786	0.000000000	10.675190	13.88456168	AT1G26680
IAA1	12.462540	3.713086	1.531036993	0.3683942	0.026348207	0.00000000	0.000000000	0.007524815	0.000000000	5.30853878	1.50761152	AT4G14560	0.3947424	5.244123	0.007524815	10.552661	6.81615030	IAA1
NAC094	17.625583	4.047506	0.006587797	1.7146054	1.039673292	0.00000000	0.000000000	0.000000000	0.000000000	6.45049619	4.36671461	AT5G39820	2.7542787	4.054093	0.000000000	10.504590	10.81721080	NAC094
LBD15	19.199671	1.994126	0.583368907	0.1850179	0.012611943	0.00000000	0.000000000	2.574826946	0.000000000	7.82336644	6.02635266	AT2G40470	0.1976298	2.577495	2.574826946	10.400861	13.84971910	LBD15
ERF9	16.487505	3.556433	0.370499429	1.4782014	0.526822244	1.40757652	0.007418725	1.389018316	0.000000000	6.37419496	1.37734056	AT5G44210	2.0050237	3.926933	2.804013560	10.301128	7.75153552	ERF9
KDR	11.626360	5.169541	4.616933686	0.6871327	0.421165730	0.12260540	0.000000000	0.000000000	0.000000000	0.46724886	0.14173293	AT1G26945	1.1082984	9.786474	0.122605397	10.253723	0.60898179	KDR
ARF16	13.237412	2.336406	1.817921968	0.5725253	0.260294865	0.00000000	0.000000000	0.000000000	0.000000000	5.44063804	2.80962553	AT4G30080	0.8328201	4.154328	0.000000000	9.594966	8.25026357	ARF16
TGA10	11.884490	4.954362	4.442996603	1.5686507	0.855081570	0.00000000	0.000000000	0.000000000	0.000000000	0.06339916	0.00000000	AT5G06839	2.4237322	9.397358	0.000000000	9.460757	0.06339916	TGA10
FEZ	15.332752	3.375534	0.000000000	1.8148960	0.697963324	0.00000000	0.000000000	0.000000000	0.000000000	5.64952955	3.79482909	AT1G26870	2.5128593	3.375534	0.000000000	9.025063	9.44435864	FEZ
ATHB13	12.940667	3.438230	4.686686725	1.2758193	1.039918586	1.30537064	0.284257457	0.000000000	0.115306229	0.74291209	0.05216550	AT1G69780	2.3157378	8.124917	1.704934322	8.867829	0.79507760	ATHB13
RITF1	16.752082	3.595258	2.290070342	1.8809351	2.633114574	1.84999357	0.000000000	0.000000000	0.000000000	2.86415361	1.63855709	AT2G12646	4.5140497	5.885328	1.849993567	8.749482	4.50271070	RITF1
ATMYC1	9.781451	3.776557	4.621138269	0.6783392	0.609503007	0.09591287	0.000000000	0.000000000	0.000000000	0.00000000	0.00000000	AT4G00480	1.2878422	8.397695	0.095912867	8.397695	0.00000000	ATMYC1
GATA17L	16.026613	3.317978	2.050499537	1.7886792	1.848639233	2.33675846	0.148609099	0.222851886	0.025486200	2.82295636	1.46415566	AT4G16141	3.6373184	5.368477	2.733705643	8.191434	4.28711202	GATA17L
AT1G22190	12.423250	3.142520	0.104379338	0.8610697	0.121072079	0.00000000	0.061920525	0.094488107	0.005574178	4.67688454	3.35534187	AT1G22190	0.9821417	3.246899	0.161982810	7.923784	8.03222642	AT1G22190
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
HSFA8	0.41967666	0.060946896	0.028177887	0.000000000	0.017208413	0.019038236	0.000000000	0.000000000	0.082472685	0.168664215	0.043168332	AT1G67970	0.017208413	0.089124783	0.101510921	0.25778900	0.211832546	HSFA8
AT4G14490	0.48144152	0.097914758	0.062991755	0.021775520	0.065075118	0.118963797	0.010144816	0.008569680	0.006095925	0.089910146	0.000000000	AT4G14490	0.086850638	0.160906513	0.143774218	0.25081666	0.089910146	AT4G14490
AT1G60700	0.40994951	0.182460085	0.007980554	0.034855645	0.000000000	0.017716305	0.000000000	0.072740257	0.020062073	0.059421663	0.014712932	AT1G60700	0.034855645	0.190440639	0.110518635	0.24986230	0.074134594	AT1G60700
AT1G77250	0.42446752	0.032777583	0.073026501	0.016693775	0.026698833	0.002863808	0.006800194	0.000000000	0.027738283	0.140674630	0.097193912	AT1G77250	0.043392609	0.105804084	0.037402286	0.24647871	0.237868542	AT1G77250
AT1G72030	0.36551917	0.073569283	0.000000000	0.021968092	0.000000000	0.048370026	0.009630455	0.000000000	0.000000000	0.161363129	0.050618187	AT1G72030	0.021968092	0.073569283	0.058000481	0.23493241	0.211981316	AT1G72030
KAPP	0.41819526	0.037603271	0.137870812	0.014134199	0.028488413	0.007540878	0.005302323	0.018861575	0.025266868	0.053232042	0.089894880	AT5G19280	0.042622612	0.175474083	0.056971644	0.22870613	0.143126922	KAPP
EMB2219	0.33585476	0.127437508	0.052771672	0.022064379	0.040583082	0.025158402	0.009630455	0.000000000	0.000000000	0.036432218	0.021777039	AT2G21710	0.062647461	0.180209180	0.034788857	0.21664140	0.058209257	EMB2219
VFP5	0.37607797	0.108112348	0.006079414	0.041069815	0.000000000	0.018602837	0.018103794	0.000000000	0.028074780	0.101070713	0.054964265	AT5G05550	0.041069815	0.114191762	0.064781412	0.21526248	0.156034979	VFP5
AT3G53440	0.29755250	0.008732049	0.067282196	0.019005436	0.035705883	0.000000000	0.002127720	0.000000000	0.000000000	0.130320835	0.034378383	AT3G53440	0.054711320	0.076014245	0.002127720	0.20633508	0.164699218	AT3G53440
SDG40	0.38524995	0.020754264	0.159293859	0.018909150	0.006692161	0.089329854	0.009535127	0.059681698	0.000000000	0.021053840	0.000000000	AT5G17240	0.025601311	0.180048123	0.158546679	0.20110196	0.021053840	SDG40
SUVH5	0.36632909	0.105951087	0.028363536	0.091486809	0.000000000	0.000000000	0.000000000	0.006320216	0.037184798	0.066305449	0.030717196	AT2G35160	0.091486809	0.134314623	0.043505014	0.20062007	0.097022645	SUVH5
TAF6B	0.34631662	0.011770842	0.025418396	0.000000000	0.002868069	0.007939713	0.000000000	0.000000000	0.036052968	0.137916985	0.124349649	AT1G54360	0.002868069	0.037189238	0.043992680	0.17510622	0.262266634	TAF6B
RAD5	0.29916515	0.059735152	0.010257687	0.010984046	0.000000000	0.044776504	0.003733243	0.005016544	0.039960460	0.093256572	0.031444947	AT5G22750	0.010984046	0.069992839	0.093486750	0.16324941	0.124701520	RAD5
AT3G46950	0.29391747	0.094381189	0.051071576	0.086813219	0.000000000	0.012038951	0.016494022	0.000000000	0.000000000	0.016081184	0.017037331	AT3G46950	0.086813219	0.145452765	0.028532974	0.16153395	0.033118514	AT3G46950
AT2G47090	0.29523820	0.035613769	0.009397183	0.008213963	0.004780115	0.000000000	0.022959660	0.006287192	0.022933347	0.107450839	0.077602132	AT2G47090	0.012994078	0.045010953	0.052180199	0.15246179	0.185052971	AT2G47090
LSMT-L	0.23580468	0.042956628	0.007096180	0.016300684	0.000000000	0.000000000	0.000000000	0.005611100	0.022608874	0.096084779	0.045146431	AT1G14030	0.016300684	0.050052808	0.028219974	0.14613759	0.141231210	LSMT-L
AT2G37000	0.17297214	0.056940627	0.080660118	0.031252544	0.000000000	0.000000000	0.004118849	0.000000000	0.000000000	0.000000000	0.000000000	AT2G37000	0.031252544	0.137600745	0.004118849	0.13760075	0.000000000	AT2G37000
CHR23	0.23323803	0.000000000	0.017757332	0.000000000	0.009560229	0.000000000	0.032041241	0.000000000	0.038840850	0.106255994	0.028782379	AT5G19310	0.009560229	0.017757332	0.070882091	0.12401333	0.135038373	CHR23
FRF1	0.19864460	0.042163366	0.080878020	0.005836971	0.028956831	0.000000000	0.026028442	0.000000000	0.014780966	0.000000000	0.000000000	AT3G59470	0.034793803	0.123041386	0.040809407	0.12304139	0.000000000	FRF1
AT3G24860	0.22581823	0.000000000	0.009373312	0.000000000	0.008604207	0.000000000	0.029893829	0.000000000	0.033688852	0.104816204	0.039441828	AT3G24860	0.008604207	0.009373312	0.063582681	0.11418952	0.144258032	AT3G24860
NBS1	0.15534389	0.000000000	0.037174125	0.021679233	0.026348207	0.000000000	0.000000000	0.000000000	0.000000000	0.070142324	0.000000000	AT3G02680	0.048027440	0.037174125	0.000000000	0.10731645	0.070142324	NBS1
MYB1	0.20603320	0.022388060	0.032148297	0.000000000	0.003824092	0.000000000	0.007847078	0.005049568	0.011571138	0.050851264	0.072353708	AT3G09230	0.003824092	0.054536357	0.024467783	0.10538762	0.123204973	MYB1
RRS1	0.13666857	0.016373195	0.040227781	0.000000000	0.035855849	0.002863808	0.005302323	0.002508272	0.008569101	0.024968238	0.000000000	AT5G45260	0.035855849	0.056600976	0.019243504	0.08156921	0.024968238	RRS1
AT3G20280	0.14763405	0.000000000	0.025533502	0.000000000	0.007648184	0.000000000	0.036230811	0.000000000	0.000000000	0.055659944	0.022561606	AT3G20280	0.007648184	0.025533502	0.036230811	0.08119345	0.078221550	AT3G20280
AT3G01890	0.14322741	0.000000000	0.068670324	0.000000000	0.055765367	0.000000000	0.013431320	0.000000000	0.000000000	0.005360395	0.000000000	AT3G01890	0.055765367	0.068670324	0.013431320	0.07403072	0.005360395	AT3G01890
AT3G51180	0.09952575	0.040510867	0.025093365	0.000000000	0.028619193	0.000000000	0.005302323	0.000000000	0.000000000	0.000000000	0.000000000	AT3G51180	0.028619193	0.065604232	0.005302323	0.06560423	0.000000000	AT3G51180
APTX	0.09391715	0.000000000	0.032778682	0.000000000	0.003824092	0.000000000	0.011203017	0.000000000	0.000000000	0.027759969	0.018351391	AT5G01310	0.003824092	0.032778682	0.011203017	0.06053865	0.046111361	APTX
AT1G61960	0.05563907	0.022946386	0.020899162	0.000000000	0.002868069	0.000000000	0.000000000	0.000000000	0.000000000	0.008925457	0.000000000	AT1G61960	0.002868069	0.043845548	0.000000000	0.05277100	0.008925457	AT1G61960
LDL2	0.03974279	0.000000000	0.019362854	0.000000000	0.002868069	0.000000000	0.013934001	0.000000000	0.000000000	0.003577863	0.000000000	AT3G13682	0.002868069	0.019362854	0.013934001	0.02294072	0.003577863	LDL2
AT2G25650	0.02857585	0.000000000	0.003293898	0.000000000	0.003824092	0.000000000	0.002726091	0.000000000	0.000000000	0.014273050	0.004458715	AT2G25650	0.003824092	0.003293898	0.002726091	0.01756695	0.018731765	AT2G25650

options(repr.plot.width=8, repr.plot.height=40)
ggplot(epilrc_rank, aes(x=reorder(GeneName, epilrc, decreasing = FALSE),  y=epilrc)) + geom_point(size=4)+ 
  labs(title="Epidermis+LRC-specific TF Prioritization",x="", y = "Combined centrality score (betweeness, out and in degree)")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(epilrc_rank,"Epidermis_LRC_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- epilrc_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Epidermis (includes LRC)", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Stele

stele_rank <- bc_rank[which(bc_rank$stele*2 > bc_rank$all),]%>% arrange(desc(stele))
stele_rank <- stele_rank[-c(match(rownames(per_rank), rownames(stele_rank)),match(rownames(pro_rank), rownames(stele_rank)),match(rownames(xyl_rank), rownames(stele_rank)),match(rownames(phl_rank), rownames(stele_rank))),]
stele_rank$GeneName <- rownames(stele_rank)

stele_rank

A data.frame: 382 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
BZIP9	31.42213	0.0000000	0.000000000	0.000000000	0.005736138	8.553166	11.880881	0.02003315	10.9623184	0.00000000	0.00000000	AT5G24800	0.005736138	0.000000000	31.41640	0.000000000	0.00000000	BZIP9
AT3G43430	29.05965	0.0000000	0.000000000	0.005443880	0.029360833	9.206565	11.246261	1.83863137	6.7333880	0.00000000	0.00000000	AT3G43430	0.034804713	0.000000000	29.02485	0.000000000	0.00000000	AT3G43430
AT1G29160	20.93074	0.0000000	0.007980554	0.000000000	0.000000000	5.300742	8.291064	0.01739278	6.9113765	0.29583475	0.10635337	AT1G29160	0.000000000	0.007980554	20.52058	0.303815309	0.40218812	AT1G29160
HAT2	28.63636	0.0000000	0.000000000	3.383183277	5.196087392	8.800066	5.980222	2.15045514	2.8538731	0.00000000	0.27247690	AT5G47370	8.579270669	0.000000000	19.78462	0.000000000	0.27247690	HAT2
LEP	33.01792	3.4900788	4.098860845	1.342756899	4.318706696	12.181573	5.664612	0.18424811	1.6862749	0.05081244	0.00000000	AT5G13910	5.661463595	7.588939598	19.71671	7.639752038	0.05081244	LEP
AT1G61660	19.23311	0.0000000	0.000000000	0.000000000	0.003824092	4.814712	9.091004	1.19149612	4.1320698	0.00000000	0.00000000	AT1G61660	0.003824092	0.000000000	19.22928	0.000000000	0.00000000	AT1G61660
MYB20	19.00219	0.0000000	0.000000000	0.000000000	0.000000000	6.531919	8.922408	1.41822212	2.1296419	0.00000000	0.00000000	AT1G66230	0.000000000	0.000000000	19.00219	0.000000000	0.00000000	MYB20
HB-8	16.99321	0.0000000	0.000000000	0.000000000	0.000000000	1.315505	6.403682	7.77220968	1.4829019	0.00000000	0.01891338	AT4G32880	0.000000000	0.000000000	16.97430	0.000000000	0.01891338	HB-8
AT5G05790	17.18280	0.0000000	0.000000000	0.003861004	0.224853405	6.253012	3.438334	0.34310536	6.9196381	0.00000000	0.00000000	AT5G05790	0.228714409	0.000000000	16.95409	0.000000000	0.00000000	AT5G05790
AT3G60490	16.33787	0.0000000	0.000000000	0.000000000	0.000000000	3.380432	6.823784	0.12843453	6.0052202	0.00000000	0.00000000	AT3G60490	0.000000000	0.000000000	16.33787	0.000000000	0.00000000	AT3G60490
GBF6	17.43899	0.0000000	0.000000000	0.205070456	0.841471637	3.189423	5.650735	2.15984172	5.3144219	0.01718324	0.06084192	AT4G34590	1.046542093	0.000000000	16.31442	0.017183238	0.07802516	GBF6
IAA12	15.30428	0.0000000	0.000000000	0.000000000	0.003074926	2.513093	6.718497	4.91679601	1.1528143	0.00000000	0.00000000	AT1G04550	0.003074926	0.000000000	15.30120	0.000000000	0.00000000	IAA12
AT2G34140	22.57599	0.1659450	0.000000000	0.052535293	0.000000000	5.437538	6.869162	0.01250833	2.8411830	4.83478544	2.36233103	AT2G34140	0.052535293	0.165945010	15.16039	5.000730448	7.19711647	AT2G34140
DAG1	15.07165	0.0000000	0.000000000	0.000000000	0.000000000	3.917813	7.064960	0.60312805	3.4857454	0.00000000	0.00000000	AT3G61850	0.000000000	0.000000000	15.07165	0.000000000	0.00000000	DAG1
GRP2B	19.35698	0.7128001	0.203070632	1.308341103	0.914635876	2.526467	2.783084	4.90924834	4.2656234	1.05946927	0.67424107	AT2G21060	2.222976979	0.915870771	14.48442	1.975340039	1.73371034	GRP2B
BT1	14.40768	0.0000000	0.000000000	0.068990233	0.203175898	2.477186	6.323171	4.01500880	1.3201442	0.00000000	0.00000000	AT5G63160	0.272166130	0.000000000	14.13551	0.000000000	0.00000000	BT1
HAT1	22.90158	1.5219280	0.214498853	1.298988901	1.003928797	4.247698	5.329100	1.84570100	2.6201942	3.23503508	1.58450292	AT4G17460	2.302917697	1.736426864	14.04269	4.971461944	4.81953800	HAT1
AT3G11280	13.67762	0.0000000	0.000000000	0.000000000	0.029399941	4.211485	4.996863	0.00000000	4.4398686	0.00000000	0.00000000	AT3G11280	0.029399941	0.000000000	13.64822	0.000000000	0.00000000	AT3G11280
AT4G30410	26.61829	2.1394912	6.023966126	3.087578399	1.537332655	4.968261	5.063052	1.55410462	1.9766307	0.22563299	0.04223994	AT4G30410	4.624911054	8.163457328	13.56205	8.389090317	0.26787293	AT4G30410
AT2G41130	13.52067	0.0886555	0.044803544	0.180941520	0.228545253	6.099147	3.212655	2.88788664	0.7780328	0.00000000	0.00000000	AT2G41130	0.409486773	0.133459043	12.97772	0.133459043	0.00000000	AT2G41130
MIF1	14.00925	0.0000000	0.000000000	0.235534657	0.849578292	4.786948	5.053819	0.00000000	2.9239998	0.09949597	0.05987183	AT1G74660	1.085112949	0.000000000	12.76477	0.099495970	0.15936780	MIF1
HB40	12.57572	0.0000000	0.009881695	0.000000000	0.000000000	1.695113	1.819935	3.67421723	5.3765778	0.00000000	0.00000000	AT4G36740	0.000000000	0.009881695	12.56584	0.009881695	0.00000000	HB40
HB-7	17.27815	0.2763827	0.134336075	1.869892003	2.384461534	4.127072	6.558106	0.10919027	1.7292334	0.00000000	0.08947662	AT2G46680	4.254353537	0.410718801	12.52360	0.410718801	0.08947662	HB-7
BZIP61	16.46509	1.0773458	0.232661098	0.831874802	1.481109147	2.552713	4.189810	2.02229437	3.5338887	0.42432748	0.11906956	AT3G58120	2.312983949	1.310006850	12.29871	1.734334333	0.54339704	BZIP61
OBP3	11.93301	0.0000000	0.000000000	0.000000000	0.000000000	2.605650	5.191763	0.02514877	4.1104477	0.00000000	0.00000000	AT3G55370	0.000000000	0.000000000	11.93301	0.000000000	0.00000000	OBP3
SGR5	11.90470	0.0000000	0.000000000	0.000000000	0.000000000	1.243737	4.920273	1.76829292	3.9724008	0.00000000	0.00000000	AT2G01940	0.000000000	0.000000000	11.90470	0.000000000	0.00000000	SGR5
AT5G51780	14.43478	0.2740037	0.183873621	0.236397042	1.958286776	4.185277	3.107451	1.78387660	2.7056188	0.00000000	0.00000000	AT5G51780	2.194683818	0.457877276	11.78222	0.457877276	0.00000000	AT5G51780
AT4G24060	12.12088	0.0000000	0.000000000	0.061593613	0.295434878	2.946540	4.410514	1.85228368	2.5545112	0.00000000	0.00000000	AT4G24060	0.357028491	0.000000000	11.76385	0.000000000	0.00000000	AT4G24060
ATAUX2-11	12.23625	0.1096186	0.003293898	0.129545999	0.000000000	1.328456	3.830691	5.73311417	0.8226915	0.10749513	0.17134443	AT5G43700	0.129545999	0.112912494	11.71495	0.220407623	0.27883956	ATAUX2-11
UNE12	11.55505	0.0000000	0.000000000	0.000000000	0.335338175	2.866596	5.008999	1.70933192	1.6347826	0.00000000	0.00000000	AT4G02590	0.335338175	0.000000000	11.21971	0.000000000	0.00000000	UNE12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
AT3G07670	0.26870441	0.000000000	0.032851353	0.051572430	0.000000000	0.112085655	0.038581244	0.000000000	0.000000000	0.027318995	0.006294737	AT3G07670	0.051572430	0.032851353	0.15066690	0.060170349	0.033613732	AT3G07670
bHLH11	0.23102660	0.022388060	0.000000000	0.000000000	0.000000000	0.077664976	0.023142061	0.000000000	0.049136649	0.023866683	0.034828172	AT4G36060	0.000000000	0.022388060	0.14994369	0.046254743	0.058694855	bHLH11
AT4G21060	0.27212465	0.000000000	0.006079414	0.000000000	0.004780115	0.002982303	0.026120529	0.007590864	0.111557120	0.058937981	0.054076329	AT4G21060	0.004780115	0.006079414	0.14825082	0.065017395	0.113014310	AT4G21060
TGA6	0.22870786	0.011770842	0.024957756	0.000000000	0.033268527	0.042397237	0.040436274	0.012508335	0.052290618	0.000000000	0.011078272	AT3G12250	0.033268527	0.036728598	0.14763246	0.036728598	0.011078272	TGA6
PCFS4	0.22631735	0.000000000	0.003293898	0.000000000	0.051620216	0.031140738	0.035721705	0.008795463	0.061766704	0.012141185	0.021837444	AT4G04885	0.051620216	0.003293898	0.13742461	0.015435083	0.033978629	PCFS4
AT1G10320	0.22091154	0.004093299	0.000000000	0.000000000	0.007648184	0.010767388	0.028665008	0.000000000	0.097396412	0.035048599	0.037292651	AT1G10320	0.007648184	0.004093299	0.13682881	0.039141898	0.072341251	AT1G10320
FRS11	0.17490242	0.000000000	0.004202144	0.000000000	0.025413486	0.004604770	0.040337371	0.049539490	0.040057134	0.000000000	0.010748023	AT1G10240	0.025413486	0.004202144	0.13453876	0.004202144	0.010748023	FRS11
AT1G62085	0.22777283	0.026927104	0.049440170	0.005443880	0.010130063	0.049121945	0.061755748	0.016320279	0.006095925	0.000000000	0.002537712	AT1G62085	0.015573943	0.076367274	0.13329390	0.076367274	0.002537712	AT1G62085
YY1	0.26203624	0.000000000	0.011654047	0.000000000	0.078151187	0.014954204	0.069636866	0.008795463	0.038622775	0.011728917	0.028492782	AT4G06634	0.078151187	0.011654047	0.13200931	0.023382964	0.040221700	YY1
MDA1	0.16234196	0.033099943	0.000000000	0.000000000	0.002868069	0.036951252	0.027019085	0.007524815	0.054878795	0.000000000	0.000000000	AT4G14605	0.002868069	0.033099943	0.12637395	0.033099943	0.000000000	MDA1
FRS3	0.21621803	0.000000000	0.007286694	0.000000000	0.039728519	0.008953307	0.003185921	0.025148766	0.087823111	0.008938258	0.035153454	AT2G27110	0.039728519	0.007286694	0.12511111	0.016224952	0.044091712	FRS3
FRS9	0.19554034	0.000000000	0.000000000	0.008213963	0.000000000	0.036374068	0.015381290	0.000000000	0.068184173	0.067386851	0.000000000	AT4G38170	0.008213963	0.000000000	0.11993953	0.067386851	0.067386851	FRS9
FRS12	0.19669530	0.000000000	0.000000000	0.000000000	0.063756702	0.032013760	0.051767129	0.008762439	0.023921025	0.000000000	0.016474242	AT5G18960	0.063756702	0.000000000	0.11646435	0.000000000	0.016474242	FRS12
AT1G62120	0.22005516	0.000000000	0.000000000	0.030182055	0.000000000	0.091132244	0.002127720	0.020097939	0.000000000	0.049223651	0.027291548	AT1G62120	0.030182055	0.000000000	0.11335790	0.049223651	0.076515198	AT1G62120
AT1G10610	0.15608756	0.000000000	0.000000000	0.000000000	0.002868069	0.004384828	0.061691231	0.022541422	0.020519242	0.006339367	0.037743406	AT1G10610	0.002868069	0.000000000	0.10913672	0.006339367	0.044082773	AT1G10610
FRS4	0.15339031	0.000000000	0.000000000	0.000000000	0.041354002	0.003151281	0.036418750	0.008795463	0.057298375	0.000000000	0.006372440	AT1G76320	0.041354002	0.000000000	0.10566387	0.000000000	0.006372440	FRS4
AT2G04740	0.18017198	0.016326397	0.035531181	0.000000000	0.011679133	0.018244257	0.022679635	0.007524815	0.052813362	0.015373197	0.000000000	AT2G04740	0.011679133	0.051857578	0.10126207	0.067230775	0.015373197	AT2G04740
AT1G61980	0.17789455	0.000000000	0.033830196	0.021775520	0.026058565	0.044767887	0.027268172	0.000000000	0.024194212	0.000000000	0.000000000	AT1G61980	0.047834085	0.033830196	0.09623027	0.033830196	0.000000000	AT1G61980
SMZ	0.18637801	0.000000000	0.014641295	0.000000000	0.003824092	0.000000000	0.018802007	0.000000000	0.075746497	0.012678080	0.060686040	AT3G54990	0.003824092	0.014641295	0.09454850	0.027319375	0.073364120	SMZ
TCP9	0.15460231	0.000000000	0.000000000	0.000000000	0.061548471	0.000000000	0.003733243	0.063558457	0.025762137	0.000000000	0.000000000	AT2G45680	0.061548471	0.000000000	0.09305384	0.000000000	0.000000000	TCP9
AT4G12850	0.13478250	0.000000000	0.041451839	0.000000000	0.002868069	0.004384828	0.000000000	0.058849233	0.027228533	0.000000000	0.000000000	AT4G12850	0.002868069	0.041451839	0.09046259	0.041451839	0.000000000	AT4G12850
NAC027	0.15560843	0.000000000	0.060812596	0.000000000	0.003824092	0.053104103	0.002127720	0.000000000	0.022616614	0.000000000	0.013123306	AT1G64105	0.003824092	0.060812596	0.07784844	0.060812596	0.013123306	NAC027
TRFL5	0.13184321	0.000000000	0.003293898	0.000000000	0.002868069	0.044776504	0.014323089	0.000000000	0.016836004	0.049745645	0.000000000	AT1G15720	0.002868069	0.003293898	0.07593560	0.053039544	0.049745645	TRFL5
U2AF35B	0.12356777	0.000000000	0.000000000	0.000000000	0.004780115	0.000000000	0.017979996	0.016881811	0.032582460	0.040008173	0.011335219	AT5G42820	0.004780115	0.000000000	0.06744427	0.040008173	0.051343393	U2AF35B
TAF12	0.11331979	0.000000000	0.004686656	0.000000000	0.011472275	0.041935595	0.017108604	0.000000000	0.007108071	0.018946504	0.012062083	AT3G10070	0.011472275	0.004686656	0.06615227	0.023633160	0.031008587	TAF12
TTR1	0.11847016	0.000000000	0.003293898	0.000000000	0.002868069	0.022680373	0.000000000	0.000000000	0.039403136	0.024848451	0.025376229	AT5G45050	0.002868069	0.003293898	0.06208351	0.028142349	0.050224680	TTR1
EMB93	0.07726139	0.000000000	0.003293898	0.000000000	0.007648184	0.000000000	0.016987805	0.020403999	0.022896464	0.000000000	0.006031042	AT2G03050	0.007648184	0.003293898	0.06028827	0.003293898	0.006031042	EMB93
NF-YB3	0.05277951	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.012766319	0.014790823	0.025222370	0.000000000	0.000000000	AT4G14540	0.000000000	0.000000000	0.05277951	0.000000000	0.000000000	NF-YB3
AT5G06420	0.06037783	0.000000000	0.011520326	0.005836971	0.009560229	0.000000000	0.011203017	0.000000000	0.022257289	0.000000000	0.000000000	AT5G06420	0.015397201	0.011520326	0.03346031	0.011520326	0.000000000	AT5G06420
MYB64	0.04324678	0.000000000	0.005195039	0.005443880	0.000000000	0.000000000	0.000000000	0.020403999	0.012203865	0.000000000	0.000000000	AT5G11050	0.005443880	0.005195039	0.03260786	0.005195039	0.000000000	MYB64

options(repr.plot.width=6, repr.plot.height=4)
ggplot(stele_rank[1:10,], aes(x=reorder(GeneName, stele, decreasing = FALSE),  y=stele)) + geom_point(size=4)+ 
  labs(title="Stele-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(stele_rank,"Stele_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- stele_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Stele", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Root Cap

rc_rank <- bc_rank[which(bc_rank$rc*2 > bc_rank$all),]%>% arrange(desc(rc))
rc_rank <- rc_rank[-c(match(rownames(lrc_rank), rownames(rc_rank)),match(rownames(col_rank), rownames(rc_rank))),]
rc_rank$GeneName <- rownames(rc_rank)

rc_rank

A data.frame: 186 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
NAI1	26.293062	4.5314653	2.427442200	0.44536331	0.205157772	0.000000000	0.000000000	0.000000000	0.000000000	10.314757	8.368876	AT2G22770	0.650521086	6.9589075	0.000000000	17.273665	18.683633	NAI1
BT2	21.465456	3.1938182	1.318831580	0.18657044	0.008604207	0.000000000	0.000000000	0.000000000	0.000000000	8.804608	7.953024	AT3G48360	0.195174642	4.5126498	0.000000000	13.317258	16.757632	BT2
PLT1	23.975143	3.9406082	0.000000000	2.00953127	1.492057686	0.056998421	0.000000000	0.000000000	0.000000000	9.148008	7.327940	AT3G20840	3.501588961	3.9406082	0.056998421	13.088616	16.475948	PLT1
AT1G32700	20.287087	0.1296096	0.000000000	0.04078096	0.127253345	0.687116252	1.191763395	0.276300728	2.201376646	6.967438	8.665448	AT1G32700	0.168034301	0.1296096	4.356557021	7.097048	15.632886	AT1G32700
AT1G36060	19.317669	3.2888697	0.911066727	0.41545573	0.417050853	0.014384784	0.000000000	0.000000000	0.000000000	7.431263	6.839578	AT1G36060	0.832506586	4.1999364	0.014384784	11.631200	14.270841	AT1G36060
AT1G26680	17.564236	2.1107861	0.000000000	1.49577760	0.073110164	0.000000000	0.000000000	0.000000000	0.000000000	8.564404	5.320157	AT1G26680	1.568887761	2.1107861	0.000000000	10.675190	13.884562	AT1G26680
LBD15	19.199671	1.9941258	0.583368907	0.18501785	0.012611943	0.000000000	0.000000000	2.574826946	0.000000000	7.823366	6.026353	AT2G40470	0.197629793	2.5774947	2.574826946	10.400861	13.849719	LBD15
CRF3	23.156426	3.7281317	2.509432523	1.56346321	1.455509716	0.227972445	0.046967234	0.000000000	0.007808814	9.414626	4.202514	AT5G53290	3.018972922	6.2375642	0.282748493	15.652190	13.617140	CRF3
SMB	18.647620	3.7951559	0.154934264	1.38856149	0.208595675	0.000000000	0.000000000	0.000000000	0.000000000	6.953647	6.146726	AT1G79580	1.597157170	3.9500901	0.000000000	10.903737	13.100372	SMB
BRON	21.210029	4.1134431	1.541404242	1.77862470	0.797459038	0.029490525	0.000000000	0.000000000	0.000000000	8.377749	4.571859	AT1G75710	2.576083735	5.6548473	0.029490525	14.032596	12.949608	BRON
AXR3	18.658641	3.6588187	0.025869489	0.64545698	0.017001939	0.994908448	2.219597404	0.011270711	0.111210919	3.821295	7.153212	AT1G04250	0.662458915	3.6846882	3.336987482	7.505983	10.974507	AXR3
NAC094	17.625583	4.0475056	0.006587797	1.71460543	1.039673292	0.000000000	0.000000000	0.000000000	0.000000000	6.450496	4.366715	AT5G39820	2.754278725	4.0540934	0.000000000	10.504590	10.817211	NAC094
AGL21	14.891367	1.5372448	0.000000000	1.92037615	0.138174292	0.220039235	0.260515696	0.017557903	0.091092158	6.146731	4.559636	AT4G37940	2.058550446	1.5372448	0.589204992	7.683976	10.706367	AGL21
AIL6	12.164641	1.1830156	0.043217853	0.72578745	0.191486449	0.000000000	0.000000000	0.000000000	0.000000000	4.812291	5.208843	AT5G10510	0.917273900	1.2262334	0.000000000	6.038525	10.021134	AIL6
SPT	13.734230	1.9285344	0.000000000	0.87954458	0.315754768	0.229104048	0.168832278	0.219305866	0.067813143	5.218644	4.706697	AT4G36930	1.195299352	1.9285344	0.685055335	7.147178	9.925341	SPT
BIM1	13.429095	1.8938991	0.183470746	0.44619358	0.174845577	0.367434232	0.395296609	0.021336822	0.388532254	5.276753	4.281332	AT5G08130	0.621039159	2.0773698	1.172599917	7.354123	9.558086	BIM1
FEZ	15.332752	3.3755338	0.000000000	1.81489600	0.697963324	0.000000000	0.000000000	0.000000000	0.000000000	5.649530	3.794829	AT1G26870	2.512859324	3.3755338	0.000000000	9.025063	9.444359	FEZ
RAV2	12.419247	0.2414081	0.008488937	0.00000000	0.004780115	0.591232196	1.826101451	0.143994572	0.318591046	4.397419	4.887231	AT1G68840	0.004780115	0.2498970	2.879919265	4.647316	9.284650	RAV2
WRKY15	11.467317	0.6642345	0.000000000	0.98076472	0.425257859	0.000000000	0.070440566	0.059492301	0.032870208	4.046072	5.188185	AT2G23320	1.406022582	0.6642345	0.162803075	4.710306	9.234257	WRKY15
WRKY9	16.059040	3.4190586	3.623664192	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	7.332666	1.683652	AT1G68150	0.000000000	7.0427228	0.000000000	14.375388	9.016318	WRKY9
ZFP5	14.356045	2.1352356	0.142448740	0.00000000	0.000000000	0.107545623	0.331890146	2.760292882	0.037982619	4.874339	3.966310	AT1G10480	0.000000000	2.2776844	3.237711271	7.152024	8.840649	ZFP5
ATL6	12.251980	1.8948191	0.045127586	1.30469935	0.129259777	0.026669574	0.265760605	0.044362394	0.133612769	4.212961	4.194708	AT3G05200	1.433959129	1.9399467	0.470405341	6.152907	8.407669	ATL6
ARF16	13.237412	2.3364059	1.817921968	0.57252527	0.260294865	0.000000000	0.000000000	0.000000000	0.000000000	5.440638	2.809626	AT4G30080	0.832820139	4.1543278	0.000000000	9.594966	8.250264	ARF16
AT1G22190	12.423250	3.1425199	0.104379338	0.86106966	0.121072079	0.000000000	0.061920525	0.094488107	0.005574178	4.676885	3.355342	AT1G22190	0.982141741	3.2468992	0.161982810	7.923784	8.032226	AT1G22190
IAA33	12.712855	1.9498474	0.080904357	1.23730199	1.444092516	0.068542296	0.000000000	0.000000000	0.000000000	4.658001	3.274165	AT5G57420	2.681394509	2.0307517	0.068542296	6.688753	7.932167	IAA33
AT1G49475	10.222529	1.2095842	0.000000000	1.19812324	0.125493723	0.036266195	0.073581493	0.000000000	0.000000000	4.845990	2.733490	AT1G49475	1.323616968	1.2095842	0.109847689	6.055575	7.579481	AT1G49475
AT1G76580	7.678801	0.2289487	0.000000000	0.03037463	0.017415270	0.004460176	0.005989591	0.000000000	0.026661484	3.535009	3.829942	AT1G76580	0.047789898	0.2289487	0.037111251	3.763958	7.364951	AT1G76580
EEL	10.895888	2.0561547	0.066651133	1.20867049	0.375224619	0.101637600	0.000000000	0.000000000	0.000000000	4.135904	2.951645	AT2G41070	1.583895114	2.1228058	0.101637600	6.258710	7.087550	EEL
AT4G39780	11.998391	0.7067230	1.427670072	0.50702444	0.401939589	0.036642163	0.082186498	0.000000000	1.804884394	3.660836	3.370484	AT4G39780	0.908964033	2.1343931	1.923713055	5.795229	7.031321	AT4G39780
IAA1	12.462540	3.7130855	1.531036993	0.36839421	0.026348207	0.000000000	0.000000000	0.007524815	0.000000000	5.308539	1.507612	AT4G14560	0.394742421	5.2441225	0.007524815	10.552661	6.816150	IAA1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
AT1G77250	0.42446752	0.032777583	0.073026501	0.016693775	0.026698833	0.002863808	0.006800194	0.000000000	0.027738283	0.140674630	0.097193912	AT1G77250	0.043392609	0.105804084	0.037402286	0.246478714	0.23786854	AT1G77250
AT3G52100	0.44581510	0.035426745	0.047247105	0.034628173	0.048239650	0.000000000	0.027733335	0.000000000	0.026288314	0.127497126	0.098754653	AT3G52100	0.082867823	0.082673851	0.054021649	0.210170977	0.22625178	AT3G52100
AT1G72030	0.36551917	0.073569283	0.000000000	0.021968092	0.000000000	0.048370026	0.009630455	0.000000000	0.000000000	0.161363129	0.050618187	AT1G72030	0.021968092	0.073569283	0.058000481	0.234932412	0.21198132	AT1G72030
HSFA8	0.41967666	0.060946896	0.028177887	0.000000000	0.017208413	0.019038236	0.000000000	0.000000000	0.082472685	0.168664215	0.043168332	AT1G67970	0.017208413	0.089124783	0.101510921	0.257788998	0.21183255	HSFA8
SPL11	0.27167266	0.000000000	0.000000000	0.000000000	0.009560229	0.000000000	0.005511606	0.000000000	0.046017186	0.097808866	0.112774773	AT1G27360	0.009560229	0.000000000	0.051528793	0.097808866	0.21058364	SPL11
IBM1	0.34821415	0.030237425	0.006281146	0.000000000	0.041100356	0.019577863	0.044373543	0.002508272	0.009581247	0.074123186	0.120431115	AT3G07610	0.041100356	0.036518572	0.076040924	0.110641758	0.19455430	IBM1
CPSF30	0.37167530	0.013690228	0.061188049	0.029700624	0.008604207	0.014903240	0.024464318	0.006287192	0.023147418	0.106973455	0.082716572	AT1G30460	0.038304830	0.074878277	0.068802168	0.181851732	0.18969003	CPSF30
AT1G76510	0.36698726	0.016110991	0.011520326	0.019005436	0.011679133	0.034858782	0.044712114	0.010033087	0.029890144	0.099989036	0.089188215	AT1G76510	0.030684569	0.027631316	0.119494127	0.127620353	0.18917725	AT1G76510
MBD8	0.31934406	0.007932070	0.005195039	0.005443880	0.025413486	0.000000000	0.030838268	0.010033087	0.047009802	0.069407100	0.118071326	AT1G22310	0.030857366	0.013127109	0.087881157	0.082534209	0.18747843	MBD8
AT4G29000	0.32909144	0.009851456	0.017757332	0.000000000	0.047968604	0.003151281	0.047400730	0.000000000	0.015607306	0.077771890	0.109582837	AT4G29000	0.047968604	0.027608788	0.066159317	0.105380678	0.18735473	AT4G29000
AT2G47090	0.29523820	0.035613769	0.009397183	0.008213963	0.004780115	0.000000000	0.022959660	0.006287192	0.022933347	0.107450839	0.077602132	AT2G47090	0.012994078	0.045010953	0.052180199	0.152461792	0.18505297	AT2G47090
AT1G29560	0.30122748	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.121965137	0.131152955	0.048109393	AT1G29560	0.000000000	0.000000000	0.121965137	0.131152955	0.17926235	AT1G29560
AT1G04390	0.26806591	0.034132838	0.008360148	0.005836971	0.028956831	0.000000000	0.018890842	0.000000000	0.003339541	0.089169324	0.079379417	AT1G04390	0.034793803	0.042492986	0.022230382	0.131662310	0.16854874	AT1G04390
AT2G24650	0.24169215	0.007932070	0.013777189	0.000000000	0.007648184	0.000000000	0.012105839	0.000000000	0.032113303	0.090433005	0.077682561	AT2G24650	0.007648184	0.021709260	0.044219143	0.112142265	0.16811557	AT2G24650
AT2G41450	0.31729877	0.016110991	0.000000000	0.030182055	0.042831723	0.055599423	0.003733243	0.000000000	0.002047342	0.103741821	0.063052174	AT2G41450	0.073013779	0.016110991	0.061380009	0.119852812	0.16679400	AT2G41450
AT3G53440	0.29755250	0.008732049	0.067282196	0.019005436	0.035705883	0.000000000	0.002127720	0.000000000	0.000000000	0.130320835	0.034378383	AT3G53440	0.054711320	0.076014245	0.002127720	0.206335080	0.16469922	AT3G53440
TAFII21	0.24289509	0.000000000	0.007980554	0.000000000	0.025413486	0.003151281	0.002127720	0.000000000	0.050117652	0.095735490	0.058368906	AT1G54140	0.025413486	0.007980554	0.055396654	0.103716044	0.15410440	TAFII21
HSF1	0.26721497	0.032673856	0.010483291	0.000000000	0.027663170	0.000000000	0.009603033	0.000000000	0.038032842	0.088981172	0.059777608	AT4G17750	0.027663170	0.043157147	0.047635876	0.132138319	0.14875878	HSF1
AT3G24860	0.22581823	0.000000000	0.009373312	0.000000000	0.008604207	0.000000000	0.029893829	0.000000000	0.033688852	0.104816204	0.039441828	AT3G24860	0.008604207	0.009373312	0.063582681	0.114189516	0.14425803	AT3G24860
LSMT-L	0.23580468	0.042956628	0.007096180	0.016300684	0.000000000	0.000000000	0.000000000	0.005611100	0.022608874	0.096084779	0.045146431	AT1G14030	0.016300684	0.050052808	0.028219974	0.146137587	0.14123121	LSMT-L
HDG11	0.19351403	0.007932070	0.017129213	0.000000000	0.003824092	0.000000000	0.017362230	0.000000000	0.007790552	0.046027562	0.093448312	AT1G73360	0.003824092	0.025061284	0.025152782	0.071088846	0.13947587	HDG11
CHR23	0.23323803	0.000000000	0.017757332	0.000000000	0.009560229	0.000000000	0.032041241	0.000000000	0.038840850	0.106255994	0.028782379	AT5G19310	0.009560229	0.017757332	0.070882091	0.124013325	0.13503837	CHR23
MYB1	0.20603320	0.022388060	0.032148297	0.000000000	0.003824092	0.000000000	0.007847078	0.005049568	0.011571138	0.050851264	0.072353708	AT3G09230	0.003824092	0.054536357	0.024467783	0.105387621	0.12320497	MYB1
AT5G41580	0.19697666	0.013690228	0.020607256	0.010984046	0.012635156	0.000000000	0.012206687	0.000000000	0.027144244	0.028538865	0.071170183	AT5G41580	0.023619202	0.034297484	0.039350931	0.062836349	0.09970905	AT5G41580
EMF2	0.19736921	0.009851456	0.007286694	0.005836971	0.024457463	0.000000000	0.002127720	0.007557840	0.041497602	0.018559839	0.080193630	AT5G51230	0.030294435	0.017138150	0.051183161	0.035697989	0.09875347	EMF2
AT3G20280	0.14763405	0.000000000	0.025533502	0.000000000	0.007648184	0.000000000	0.036230811	0.000000000	0.000000000	0.055659944	0.022561606	AT3G20280	0.007648184	0.025533502	0.036230811	0.081193446	0.07822155	AT3G20280
TCP4	0.07782803	0.000000000	0.000000000	0.000000000	0.009560229	0.000000000	0.008476926	0.000000000	0.000000000	0.029050946	0.030739923	AT3G15030	0.009560229	0.000000000	0.008476926	0.029050946	0.05979087	TCP4
AT5G07400	0.07931352	0.000000000	0.000000000	0.013465270	0.004780115	0.000000000	0.007418725	0.000000000	0.000000000	0.014273050	0.039376358	AT5G07400	0.018245385	0.000000000	0.007418725	0.014273050	0.05364941	AT5G07400
AT2G25650	0.02857585	0.000000000	0.003293898	0.000000000	0.003824092	0.000000000	0.002726091	0.000000000	0.000000000	0.014273050	0.004458715	AT2G25650	0.003824092	0.003293898	0.002726091	0.017566949	0.01873177	AT2G25650
AT4G19650	0.02016868	0.000000000	0.003293898	0.000000000	0.003824092	0.000000000	0.002127720	0.000000000	0.000000000	0.003577863	0.007345102	AT4G19650	0.003824092	0.003293898	0.002127720	0.006871762	0.01092297	AT4G19650

rc_rank[1:50,]

A data.frame: 50 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
NAI1	26.293062	4.53146528	2.427442200	0.44536331	0.205157772	0.000000000	0.000000000	0.000000000	0.000000000	10.3147574	8.368876	AT2G22770	0.650521086	6.95890748	0.000000000	17.273665	18.683633	NAI1
BT2	21.465456	3.19381823	1.318831580	0.18657044	0.008604207	0.000000000	0.000000000	0.000000000	0.000000000	8.8046078	7.953024	AT3G48360	0.195174642	4.51264981	0.000000000	13.317258	16.757632	BT2
PLT1	23.975143	3.94060825	0.000000000	2.00953127	1.492057686	0.056998421	0.000000000	0.000000000	0.000000000	9.1480076	7.327940	AT3G20840	3.501588961	3.94060825	0.056998421	13.088616	16.475948	PLT1
AT1G32700	20.287087	0.12960958	0.000000000	0.04078096	0.127253345	0.687116252	1.191763395	0.276300728	2.201376646	6.9674380	8.665448	AT1G32700	0.168034301	0.12960958	4.356557021	7.097048	15.632886	AT1G32700
AT1G36060	19.317669	3.28886970	0.911066727	0.41545573	0.417050853	0.014384784	0.000000000	0.000000000	0.000000000	7.4312633	6.839578	AT1G36060	0.832506586	4.19993643	0.014384784	11.631200	14.270841	AT1G36060
AT1G26680	17.564236	2.11078613	0.000000000	1.49577760	0.073110164	0.000000000	0.000000000	0.000000000	0.000000000	8.5644043	5.320157	AT1G26680	1.568887761	2.11078613	0.000000000	10.675190	13.884562	AT1G26680
LBD15	19.199671	1.99412577	0.583368907	0.18501785	0.012611943	0.000000000	0.000000000	2.574826946	0.000000000	7.8233664	6.026353	AT2G40470	0.197629793	2.57749468	2.574826946	10.400861	13.849719	LBD15
CRF3	23.156426	3.72813165	2.509432523	1.56346321	1.455509716	0.227972445	0.046967234	0.000000000	0.007808814	9.4146263	4.202514	AT5G53290	3.018972922	6.23756418	0.282748493	15.652190	13.617140	CRF3
SMB	18.647620	3.79515586	0.154934264	1.38856149	0.208595675	0.000000000	0.000000000	0.000000000	0.000000000	6.9536467	6.146726	AT1G79580	1.597157170	3.95009013	0.000000000	10.903737	13.100372	SMB
BRON	21.210029	4.11344307	1.541404242	1.77862470	0.797459038	0.029490525	0.000000000	0.000000000	0.000000000	8.3777491	4.571859	AT1G75710	2.576083735	5.65484731	0.029490525	14.032596	12.949608	BRON
AXR3	18.658641	3.65881868	0.025869489	0.64545698	0.017001939	0.994908448	2.219597404	0.011270711	0.111210919	3.8212951	7.153212	AT1G04250	0.662458915	3.68468817	3.336987482	7.505983	10.974507	AXR3
NAC094	17.625583	4.04750565	0.006587797	1.71460543	1.039673292	0.000000000	0.000000000	0.000000000	0.000000000	6.4504962	4.366715	AT5G39820	2.754278725	4.05409345	0.000000000	10.504590	10.817211	NAC094
AGL21	14.891367	1.53724482	0.000000000	1.92037615	0.138174292	0.220039235	0.260515696	0.017557903	0.091092158	6.1467312	4.559636	AT4G37940	2.058550446	1.53724482	0.589204992	7.683976	10.706367	AGL21
AIL6	12.164641	1.18301557	0.043217853	0.72578745	0.191486449	0.000000000	0.000000000	0.000000000	0.000000000	4.8122911	5.208843	AT5G10510	0.917273900	1.22623342	0.000000000	6.038525	10.021134	AIL6
SPT	13.734230	1.92853440	0.000000000	0.87954458	0.315754768	0.229104048	0.168832278	0.219305866	0.067813143	5.2186441	4.706697	AT4G36930	1.195299352	1.92853440	0.685055335	7.147178	9.925341	SPT
BIM1	13.429095	1.89389909	0.183470746	0.44619358	0.174845577	0.367434232	0.395296609	0.021336822	0.388532254	5.2767535	4.281332	AT5G08130	0.621039159	2.07736984	1.172599917	7.354123	9.558086	BIM1
FEZ	15.332752	3.37553381	0.000000000	1.81489600	0.697963324	0.000000000	0.000000000	0.000000000	0.000000000	5.6495295	3.794829	AT1G26870	2.512859324	3.37553381	0.000000000	9.025063	9.444359	FEZ
RAV2	12.419247	0.24140805	0.008488937	0.00000000	0.004780115	0.591232196	1.826101451	0.143994572	0.318591046	4.3974193	4.887231	AT1G68840	0.004780115	0.24989699	2.879919265	4.647316	9.284650	RAV2
WRKY15	11.467317	0.66423449	0.000000000	0.98076472	0.425257859	0.000000000	0.070440566	0.059492301	0.032870208	4.0460720	5.188185	AT2G23320	1.406022582	0.66423449	0.162803075	4.710306	9.234257	WRKY15
WRKY9	16.059040	3.41905862	3.623664192	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	7.3326656	1.683652	AT1G68150	0.000000000	7.04272281	0.000000000	14.375388	9.016318	WRKY9
ZFP5	14.356045	2.13523562	0.142448740	0.00000000	0.000000000	0.107545623	0.331890146	2.760292882	0.037982619	4.8743394	3.966310	AT1G10480	0.000000000	2.27768435	3.237711271	7.152024	8.840649	ZFP5
ATL6	12.251980	1.89481914	0.045127586	1.30469935	0.129259777	0.026669574	0.265760605	0.044362394	0.133612769	4.2129608	4.194708	AT3G05200	1.433959129	1.93994673	0.470405341	6.152907	8.407669	ATL6
ARF16	13.237412	2.33640587	1.817921968	0.57252527	0.260294865	0.000000000	0.000000000	0.000000000	0.000000000	5.4406380	2.809626	AT4G30080	0.832820139	4.15432784	0.000000000	9.594966	8.250264	ARF16
AT1G22190	12.423250	3.14251987	0.104379338	0.86106966	0.121072079	0.000000000	0.061920525	0.094488107	0.005574178	4.6768845	3.355342	AT1G22190	0.982141741	3.24689921	0.161982810	7.923784	8.032226	AT1G22190
IAA33	12.712855	1.94984738	0.080904357	1.23730199	1.444092516	0.068542296	0.000000000	0.000000000	0.000000000	4.6580013	3.274165	AT5G57420	2.681394509	2.03075174	0.068542296	6.688753	7.932167	IAA33
AT1G49475	10.222529	1.20958417	0.000000000	1.19812324	0.125493723	0.036266195	0.073581493	0.000000000	0.000000000	4.8459904	2.733490	AT1G49475	1.323616968	1.20958417	0.109847689	6.055575	7.579481	AT1G49475
AT1G76580	7.678801	0.22894869	0.000000000	0.03037463	0.017415270	0.004460176	0.005989591	0.000000000	0.026661484	3.5350092	3.829942	AT1G76580	0.047789898	0.22894869	0.037111251	3.763958	7.364951	AT1G76580
EEL	10.895888	2.05615467	0.066651133	1.20867049	0.375224619	0.101637600	0.000000000	0.000000000	0.000000000	4.1359044	2.951645	AT2G41070	1.583895114	2.12280581	0.101637600	6.258710	7.087550	EEL
AT4G39780	11.998391	0.70672300	1.427670072	0.50702444	0.401939589	0.036642163	0.082186498	0.000000000	1.804884394	3.6608363	3.370484	AT4G39780	0.908964033	2.13439307	1.923713055	5.795229	7.031321	AT4G39780
IAA1	12.462540	3.71308551	1.531036993	0.36839421	0.026348207	0.000000000	0.000000000	0.007524815	0.000000000	5.3085388	1.507612	AT4G14560	0.394742421	5.24412250	0.007524815	10.552661	6.816150	IAA1
AT5G03510	12.378022	0.35100228	0.031149052	0.00000000	0.137191871	0.021307799	0.020918106	4.975736906	0.109229651	1.5322636	5.199223	AT5G03510	0.137191871	0.38215133	5.127192462	1.914415	6.731486	AT5G03510
PLT2	12.093601	3.21355999	0.000000000	1.35960481	1.006404015	0.073726311	0.000000000	0.000000000	0.000000000	3.9988320	2.441474	AT1G51190	2.366008823	3.21355999	0.073726311	7.212392	6.440306	PLT2
COL5	8.235688	1.36191425	0.164686808	0.06105435	0.064236712	0.069729090	0.236178921	0.010033087	0.007796400	2.4645421	3.795516	AT5G57660	0.125291066	1.52660105	0.323737498	3.991143	6.260058	COL5
BBX30	9.719073	0.70769180	0.000000000	0.93677231	0.240096728	0.211833771	0.514105862	0.881010141	0.082920728	2.7907198	3.353922	AT4G15248	1.176869034	0.70769180	1.689870502	3.498412	6.144642	BBX30
ATCTH	10.114217	1.79162690	1.005841023	0.33627441	0.226812548	0.153039198	0.194615958	0.597982220	0.105784934	4.1070802	1.595159	AT2G25900	0.563086961	2.79746793	1.051422311	6.904548	5.702239	ATCTH
CHR38	7.658272	0.83781011	0.000000000	0.83862016	0.054505103	0.014690742	0.141227245	0.000000000	0.069894227	3.1032199	2.598305	AT3G42670	0.893125265	0.83781011	0.225812214	3.941030	5.701525	CHR38
NAC083	9.118027	0.34584979	0.008448429	0.86664745	1.226519613	0.303323618	0.646445691	0.010066111	0.345777910	0.8430538	4.521895	AT5G13180	2.093167063	0.35429822	1.305613331	1.197352	5.364949	NAC083
AT3G57800	6.093701	0.15638978	0.025466812	0.03386951	0.000000000	0.063042752	0.110701942	0.005049568	0.370642324	2.5749567	2.753582	AT3G57800	0.033869507	0.18185659	0.549436586	2.756813	5.328538	AT3G57800
CZF1	8.084519	0.29333471	0.016617717	0.35008676	0.220860228	0.586271506	0.811711320	0.011270711	0.702898467	3.2191331	1.872334	AT2G40140	0.570946983	0.30995243	2.112152004	3.529086	5.091468	CZF1
HSFA7A	6.438350	0.12687303	0.007096180	0.16904460	0.107025362	0.173020652	0.336185481	0.000000000	0.587030448	2.3208992	2.611175	AT3G51910	0.276069961	0.13396921	1.096236581	2.454868	4.932074	HSFA7A
AT1G74840	7.274395	0.39570162	0.954840046	0.43717474	0.336124810	0.088868898	0.104954759	0.518120918	0.050515540	2.9309542	1.457139	AT1G74840	0.773299554	1.35054166	0.762460115	4.281496	4.388093	AT1G74840
AT2G35910	4.636897	0.23554965	0.059364086	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	2.2327001	2.109284	AT2G35910	0.000000000	0.29491374	0.000000000	2.527614	4.341984	AT2G35910
RAP2.1	5.111611	0.00000000	0.000000000	0.47448042	0.011242013	0.000000000	0.006726091	0.771326813	0.000000000	2.0164139	1.831422	AT1G46768	0.485722429	0.00000000	0.778052904	2.016414	3.847836	RAP2.1
AT4G25400	5.561647	1.01173343	0.519961069	0.00000000	0.095528317	0.141937742	0.224447786	0.000000000	0.036259813	1.2244679	2.307311	AT4G25400	0.095528317	1.53169450	0.402645341	2.756162	3.531779	AT4G25400
ARF6	4.612046	0.50660587	0.033314975	0.48257114	0.109948770	0.000000000	0.000000000	0.000000000	0.000000000	1.3039451	2.175660	AT1G30330	0.592519907	0.53992085	0.000000000	1.843866	3.479605	ARF6
RDUF1	4.229145	0.44002126	0.035950103	0.44208233	0.069319299	0.000000000	0.006904364	0.000000000	0.030089012	1.2783165	1.926462	AT3G46620	0.511401624	0.47597137	0.036993376	1.754288	3.204778	RDUF1
WRKY14	4.012880	0.63393072	0.225698358	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	1.6502378	1.503014	AT1G30650	0.000000000	0.85962908	0.000000000	2.509867	3.153251	WRKY14
SCL8	4.456934	0.09199188	0.003992795	0.10860697	0.050594655	0.042578034	0.101105099	0.795219219	0.167179674	1.2943751	1.801291	AT5G52510	0.159201620	0.09598468	1.106082026	1.390360	3.095666	SCL8
BHLH101	3.703793	0.23238764	0.000000000	0.11843957	0.009174040	0.000000000	0.000000000	0.000000000	0.302435295	1.3438272	1.697529	AT5G04150	0.127613610	0.23238764	0.302435295	1.576215	3.041357	BHLH101
PIF3	3.867586	0.27585520	0.000000000	0.31966574	0.041917461	0.011872211	0.000000000	0.000000000	0.402717727	1.3576067	1.457951	AT1G09530	0.361583199	0.27585520	0.414589938	1.633462	2.815558	PIF3

options(repr.plot.width=6, repr.plot.height=4)
ggplot(rc_rank[1:10,], aes(x=reorder(GeneName, rc, decreasing = FALSE),  y=rc)) + geom_point(size=4)+ 
  labs(title="Root Cap-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(rc_rank,"Root_Cap_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- rc_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Root Cap", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))